SMEs Use AI-Driven Financial Software for Greater Efficiency

SMEs Use AI-Driven Financial Software for Greater Efficiency

AI is driving major changes in the financial world. It is estimated that Fintech companies spent over $9.5 billion on AI in 2021, but small businesses may spend even more on AI-driven financial management software.

The banking industry is among those most heavily affected by AI. Smart solutions can give banks an advantage over competitors. Some of the benefits of AI in banking include:

Banks use AI bots to onboard clients and analyze borrower risk. Forbes author Rob Shevlin reports that chatbots will actually make bank branches obsolete.

They have also started integrated computer vision and deep learning technology to identify inefficiencies.

AI-based anti-money laundering solutions are also being used to prevent fraud. Patrick Craig, the Ernst and Young EMEIA Financial Crime Technology Lead stated that AI can help financial institutions better keep up with their efforts to fight fraud.

Banks and other financial institutions are combining AI with other technologies to transform their business models. For example, Infosys helped an Australian bank predict demand, consumption, and price for trading companies. The dashboard streamlined their business trading and procurement process.

While AI can have huge implications for large financial institutions, it is also changing the financial strategy for small businesses as well. Many small businesses are investing in AI-driven financial management software. Upgrading your tech stack is a big undertaking. Many businesses fail to take full advantage of the resources that are available to them simply because they aren’t sure how to get started. The problem is you don’t want to stay analog while your competitors are up in the “Cloud.”

AI-based financial tools aren’t just for your accountants. They can be an invaluable asset for your entire business. In this article, we look at the importance of financial software and discuss how you can use it to secure better business outcomes. Keep reading to learn more about the relevance of AI in finance.

The Evolution of Fintech

For decades the most important technological innovation in finance was the calculator. As AI technology began to work its way into offices all across the country, experts made bold predictions. Financial technology (FinTec) wouldn’t just make accountants’ lives easier. It would replace them altogether.

In the early 2000s, articles were being written that suggested accounting would no longer exist as a profession in the next several decades  (in other words, right about now). Obviously, that did not happen. However, AI has changed the state of the profession for better or worse.

Part of the reason for that is that FinTec isn’t quite there yet. Automation is good for taking on repetitive tasks, so AI is a lifesaver for companies with many monotonous tasks. When variables enter the equation, manual effort and human oversite are both necessary.

The other thing? These accountants who now have digitalized their jobs aren’t sitting around useless. They use their free time to focus on more fruitful efforts, so AI has helped them do more important things.

That is often the end game for digital tech implementation. A good financial tech stack that incorporates AI into its models allows you to:

Scale: Growing pains are very real. When a company begins to expand its business things start to change. Suddenly, you have all of your previous responsibilities, plus a new challenge: How do we operate at the same peak efficiency while serving twice as many people? Digital technology allows you to transition into growth without endlessly expanding your departments.

Focus on the bigger picture: While the software handles small stuff, your accountants and other financial professionals can help leverage their time toward bigger goals. Planning out an expansion. Thinking about the financial components of product development, etc. Of course, you would need their help for these things eventually, but now it can happen quicker and with fewer distractions.

A great Fintech lineup may trim your staff somewhat. This is particularly true for companies that were previously making lots of hires to keep up with their growing businesses’ new demands. However, digital technology hasn’t been nearly as much of a job killer as many people once assumed.

Examples of AI in Fintech

Like so many other aspects of workplace digitization, your Fintech stack will usually be made up of many tools that utilize AI. Your accounts will have software specific to accounting. Your analysts might have software designed to help with business forecasting. Billing will have software to manage invoices and payment processing.

It sounds expensive.

It is! Software is now typically a monthly recurring cost. Each tool you acquire may have a relatively low subscription fee, but these costs add up. The benefits of the Software as a Service model (wherein you never own your software but simply rent it) do tend to outweigh the cons. Benefits include:

Free updates: It used to be that you would buy software, and hang on to it for as long as you could. This might mean using the same program for ten-plus years. Frugal, sure, but also a bit of a hindrance. Tech companies are constantly updating their products. Keeping your software up-to-date can help you secure a competitive advantage.

Easier startup cost: Instead of spending tens of thousands of dollars on the front end to acquire all of your tools, you can instead lease them at a much more achievable price. Better yet, because you’re just a renter, it’s easy to pivot into new tools if your first choice doesn’t work out the way you hoped it would.

You’ll still pay a pretty penny for tech. However, part of the promise is that when you use digital technology the right way, it usually pays for itself.

The Right Way

Unfortunately, acquiring software isn’t only about finding the best of every product. You do want excellent tech solutions, but you also want programs that work well together. Unfortunately, that is often easier said than done.

The key word here is “integrations.” That’s the phrase tech folk use to describe how well various tools interact and communicate with one another. Some tools are designed specifically to link up and integrate. These tools will be well adapted for sharing data between departments and generally optimizing your operations.

Tools that don’t integrate can result in “data siloes.” In these situations, your business has all of the data it could ever want, but not in places that are accessible. Accounting has data here, sales has data there, and never shall the two meet.

Why does sales need to be able to look at billing’s data?

Let’s say you want to start focusing more on upsells. You need your sales team to go out, and find the people most willing to not only buy your products but buy the premium version. First, you need to figure out what sort of person is currently doing that the most.

Guess who has the information? Billing.

Separation between departments is largely an imaginary concept. Your business has a broad set of goals, and every department is contributing toward said goals in the best way that they can. Integrations make this job much easier.

If you don’t feel up to the task of choosing the right tech solutions, some consultants can help advise you. They will charge a fee, of course, but it will be much less expensive than the cost of constantly revamping your tech stack.

AI is Changing Finance

AI is certainly the future. There is no doubt that it is changing the state of finance. More companies will need to use AI-driven software to improve their financial services models.

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Growing Demand for Data Science & Data Analyst Roles

Growing Demand for Data Science & Data Analyst Roles

Unleash your analytical prowess in today’s most coveted professions – Data Science and Data Analytics! As companies plunge into the world of data, skilled individuals who can extract valuable insights from an ocean of information are in high demand. Join the data revolution and secure a competitive edge for businesses vying for supremacy.

Data Scientists and Analysts use various tools such as machine learning algorithms, statistical modeling, natural language processing (NLP), and predictive analytics to identify trends, uncover opportunities for improvement, and make better decisions. With the right combination of technical know-how, communication skills, problem solving abilities, and creative thinking – these professionals can help organizations gain a competitive advantage by leveraging data effectively.

The skills required for a successful career in data science and data analysis

Data science and data analysis have rapidly emerged as flourishing and versatile career paths, encompassing a wide range of industries and applications. Essential skills for aspiring professionals in these fields include a solid foundation in mathematics, which is crucial for understanding statistical models and algorithms, and analytical abilities to uncover valuable insights from complex datasets. Proficiency in various programming languages, such as Python, R, and SQL, empowers individuals to efficiently manipulate and visualize data, thus enhancing the decision-making process for businesses. Equally important is the ability to communicate effectively, presenting data-driven solutions to stakeholders in a clear and concise manner. Pursuing a successful career in data science and data analysis demands an innate curiosity to identify trends, unravel hidden patterns, and possess a keen problem-solving mindset, transforming complex data into actionable strategies.

Understanding the job market for these roles

The demand for data science and data analysis professionals is increasingly growing, as businesses are increasingly leveraging data-driven methods to stay competitive. According to Forbes Insights, the number of jobs in these fields will continue to rise in 2021 – with Data Scientist positions expected to grow by 32% over the next five years. These roles are highly prized among employers, and specialized talent is in high demand. Companies are also investing heavily in data science initiatives, with an increasing number of corporations building out their own analytics teams to stay ahead of the curve.

With the right skills, qualifications and experience, data scientists and analysts can secure high-paying jobs in top tech companies such as Google, Amazon, Microsoft and Facebook. Alternatively, these professionals can also choose to become independent consultants – leveraging their expertise to help businesses make better decisions.

Exploring different educational pathways to become a data scientist or analyst

If you’ve ever wondered about becoming a data scientist or analyst, you’ll be glad to know that there’s no one-size-fits-all approach. Traditional paths, like earning a degree in computer science, statistics, or mathematics, can provide you with a solid foundation in the subject. Yet, there are other exciting avenues to explore: self-paced online courses, IT trade schools, boot camps, and even pursuing an interdisciplinary degree combining data science principles with another field of study. Moreover, experimenting with real-world projects or participating in competitions and internships can give you that essential hands-on experience to stand out. 

Tips on how to stand out as a candidate when applying for these roles

When it comes to competing for data science and analysis jobs, employers usually look for candidates who can bring something extra to the table. Here are some tips on how to stand out as a candidate:

Build an impressive portfolio of work that showcases your skills, expertise and experience in the field.

Participate in online competitions or hackathons to demonstrate your ability to address challenging problems with creative solutions.

Develop relationships with experienced professionals in the industry and gain practical knowledge through mentoring opportunities.

Make sure you have a good understanding of relevant software and technologies in the field such as big data platforms, machine learning algorithms, natural language processing tools etc., as this will set you apart from other applicants.

Wrapping Up

In conclusion, the demand for data Science and Data Analysts is higher than ever and will continue to rise in the foreseeable future. These crucial roles are essential to businesses of all sizes, providing insights that no other profession can deliver. To be successful in this field requires a diverse set of skills, ranging from technical to analytical proficiency. Understanding the job market is key to setting oneself apart from the competition, as there are differences between companies in regards to educational requirements. Additionally gaining a deep understanding of how companies use data science and analytics will provide invaluable experience. Therefore, by developing these skills, staying informed about emerging trends and being flexible with educational pathways will make any data scientist or analyst shine in the current highly competitive job market.

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6 Reasons to Boost Data Security Plan in the Age of Big Data

6 Reasons to Boost Data Security Plan in the Age of Big Data

Our lives are being affected by big data more than ever. Therefore, it should be no surprise that the big data analytics market is projected to be worth $655 billion by 2027.

However, the rise of big data has also led to greater security risks. Businesses need to bolster their data security as a result.

Our Growing Dependence on Big Data Makes Data Security More Important than Ever

Big data is revolutionizing the way we live our lives. From healthcare to finance and from social media to education, big data is transforming how we interact with the world around us.

It enables organizations to make more informed decisions, develop more efficient products and services, and gain a better understanding of their customers. With big data, companies can identify patterns in customer behavior that can help them improve their products and services. It also helps them gain insights into customer preferences that can help them create better marketing campaigns and strategies. Big data is changing our lives in ways that were never possible before and will continue to do so in the future.

Sadly, the proliferation of big data comes at a cost. More organizations are vulnerable to data breaches, which requires them to improve their data security efforts.

This is your sign to look at your organization’s data security plan and see if there are holes and gaps that need to be filled. The truth is, data is imperative for the success of your business. You need to ensure that the data you collect is safe and secure and that you understand all the ways that tricky hackers try to get into your systems. Emails, bad links, phone scams, and more can all be used to steal company information and tank your business. Here are the top 6 reasons why you need to reconsider your lax data security plan.

Cybercrime Continues to Rise

Each year, cybercrime costs the economy billions of dollars. According to the Digital Guardian, cybercrime cost the U.S. $6.9 billion in 2021 alone.

Not only that, but a big breach can destroy a small business. It’s important to take data security seriously and look beyond just having antivirus software on your computer. One of the reasons many companies don’t prioritize data security is that they believe they’re not at risk of being attacked. This is not true.

Threats are getting more sophisticated, which is why it’s so important to stay on top of data security concerns. Since many small businesses don’t know how to handle this, they can easily outsource to a third-party risk management company, or TPRM. These companies can help you evaluate your risk and help you find ways to reduce it.

Your Business Data is at Risk

Your business data is at risk from cybercrime. You won’t be able to protect your company for long if you have a lax data security plan. But, you can implement processes and applications that can protect your business. Hackers want nothing more than to get ahold of phone numbers, addresses, social security numbers, and more. Plus, some competitors may fight dirty and hire hackers to try and compromise your systems.

You Can Be Held Liable for Data Breaches

This is a hard pill for many businesses to swallow. Your business is responsible for the data that your customers give to you. Not only that, but you are also responsible for vendor data. If you fail to protect their information, they have every right to sue you. If your data security plan is too lax, then it’s likely they can win the case. Additionally, the government can also fine you if you don’t comply with regulations.

Employees Use Unsecured Apps Like Social Media

Another reason to reconsider a relaxed approach is that employees are using more and more social media and other unsecured apps for work. While this may seem like a convenient way to stay in touch with your colleagues and clients, it’s also a major source of data breaches. In fact, one study found that most enterprise mobile devices contain at least one app that poses a threat to corporate security or compliance practices. Most employees are unaware of these issues these apps can cause themselves or the company.

A Good Malware Attack Could Cost You Millions

Hackers are smart and they use people’s ignorance to their advantage. The truth is that a good malware attack can cost you millions of dollars. Whether it’s a ransomware attack that locks up your data and demands payment or an insider breach where sensitive information is stolen, the costs associated with these breaches are staggering.

Companies Have Way More Breaches Than You Realize

Just because you don’t hear it on the news doesn’t mean that it’s not happening. The big Target breach wasn’t the only issue they ever experienced, and it certainly won’t be the last. It was simply one of the biggest, wide-reaching, and most damaging breaches that companies had experienced up to that point. You won’t hear on the news when your favorite companies have a breach unless they are required to by law. Companies need to be smart about protecting their data and their customers and staff. Data security processes and procedures help to do just that.

Data Security Issues Shouldn’t Be Overlooked in a World Governed by Big Data

There are more reasons to have a good security plan in place. You simply can’t ignore the importance of them in the big data age.

The important thing is to understand all of your liabilities and be able to leverage the right technology and processes to provide a more secure environment. This can help you protect company information, keep your customer data safe, and thrive in your business because you won’t need to worry about hackers breaking things. 

Source : SmartData Collective Read More

5 big things you can do at Google Data Cloud & AI Summit this week

5 big things you can do at Google Data Cloud & AI Summit this week

Data is at the heart of digital transformation and organizations are looking to find new opportunities to transform customer experiences, boost revenue, and reduce costs. In a new study conducted by Harvard Business Review Analytic Services for Google Cloud, 91% percent of leaders say that democratizing access to data is imperative to business success, and 76% say democratized access to artificial intelligence (AI) is critical. Recent advancements in AI have companies building their generative AI strategy — including how to build generative AI applications to drive business value, customize foundation models to meet their needs, and help ensure control over their data privacy. 

Join us at the Google Data Cloud & AI Summit ( Mar 29 Americas, Mar 30 EMEA) to reveal opportunities to transform your business. You’ll gain expert insights and learn about the latest innovations in Google Data Cloud for AI, databases, data analytics, and business intelligence.

Here are five big things you can do at the digital event.

#1: Get inspired with opening keynotes

June Yang and Lisa O’Malley will kick off the summit with a keynote session on what’s new in generative AI from Google Cloud. You will learn about the latest trends in AI and get a closer look at our recent announcements such as Generative AI App Builder and Generative AI support on Vertex AI. The keynote will showcase real-world use cases and you will get to see these new products in action with demos. Gerrit Kazmaier and Andi Gutmans will deliver the second keynote session on the latest innovations in Google Data Cloud and how you can use data and AI to reveal the next era of innovation and efficiency.

#2: Catch the latest announcements

Get the scoop on Google Cloud’s vision for a unified, open, intelligent data cloud. You’ll also be one of the first to learn the newest innovations across products like BigQuery, AlloyDB, Looker, and Vertex AI. 

#3: Go deeper with top experts in data and AI

After the keynotes, drill deep on topics that matter to you with fourteen breakout sessions across two tracks — AI Innovation and Data Essentials. The AI Innovation track will feature topics such as how to build generative AI apps in minutes, build, customize and deploy foundation models in Vertex AI, and activate your data with AI. The Data Essentials track will cover topics such as what’s new in databases, BigQuery, and Looker, and how to bring cross-cloud analytics to your data with a unified analytics lakehouse.  

#4: Get insights from customers, industry experts and partners

You will hear from customers across the globe who are solving complex data challenges with Google Cloud, including Dun & Bradstreet,, Orange, ShareChat, Oakbrook Finance, Richemont and CI&T. 

Bruno Aziza will also host a Q&A with Baris Gul, Director of Engineering and Warren Qi, Engineering Manager at about how they’re unlocking new potential to accelerate development cycle, reducing time to market from months to weeks. Ritika Gunnar and Azitabh Ajit, Director of Engineering, Data & Tech Platform, ShareChat will share how organizations can build data-rich applications faster and enable innovation. 

Yasmeen Ahmad will discuss with Sanjeev Mohan, Founder at SanjMo & Former VP at Gartner, various cost-saving strategies and how to use the intelligent financial-operation capabilities of Google Data Cloud products to control, plan, forecast, and optimize data analytics and AI costs effectively.

You’ll also learn how you can innovate faster and accelerate digital transformation with solutions from partners such as SAP, Databricks, Tabnine, and MongoDB.

#5: See it in action through demos

Throughout the Summit experience, there’ll be many opportunities for hands-on learning. Jason Davenport and Gabe Weiss will walk through an end-to-end demo of how the latest innovations in the Data Cloud come together to improve the customer experience for an online boutique application, including code examples. This demo will show how these new capabilities are used and provide developers a reference to go play with the demo on their own. 

In addition, you’ll find exciting interactive demos, videos, hands-on labs, and learning paths on the Summit website to build your skills and continue your data and AI journey after attending the sessions. 

We are excited to share what we’ve been working on. Save your spot today by registering on the Data Cloud & AI Summit homepage (Mar 29 Americas, Mar 30 EMEA). We hope to see you there.

Source : Data Analytics Read More

Workload Identity for GKE made easy with open source tools

Workload Identity for GKE made easy with open source tools

Google Cloud offers a clever way of allowingGoogle Kubernetes Engine (GKE) workloads to safely and securely authenticate to Google APIs with minimal credentials exposure. I will illustrate this method using a tool called kaniko.

What is kaniko?

kaniko is an open source tool that allows you to build and push container images from Kubernetes pods when a Docker daemon is not easily accessible and you have no root access to the underlying machine. kaniko executes the build commands entirely in the userspace and has no dependency on the Docker daemon. This makes it a popular tool in continuous integration (CI) pipeline toolkits.

The dilemma

Suppose you want to access some Google Cloud – services from your GKE workload such as a secret fromSecret Manager, or in our case: build and push a container image to Google’sContainer Registry (GCR). However, it requires authorization of a Google service account (GSA) governed byCloud IAM. This is different from a Kubernetes service account (KSA) which provides an identity for pods and is dictated by its own Kubernetes Role-Based Access Control (RBAC). So how would you go about providing access to your GKE workloads to said Google Cloud services in a secure manner? 

1: Use the Compute Engine service account

The first option is to leverage the IAM service account used by the node pool(s). By default, this would be theCompute Engine default service account. The downside to this method is that the permissions of the service account is shared by all workloads, violating the principle of least privilege. Because of this, it is recommended that you use a custom service account with theleast privileged role and opt for a more granular approach when providing access to your workloads.

2: Use service account keys as Kubernetes secrets

The more secure second option is the tried, tested, and true method to generate account keys for a Google SA with the permissions that you need and mount them in your pod as aKubernetes secret. The pod manifest to build and push an image to GCR would look something like the following:

code_block[StructValue([(u’code’, u’apiVersion: v1rnkind: Podrnmetadata:rn name: kaniko-k8s-secretrnspec:rn containers:rn – name: kanikorn image: args: [“–dockerfile=Dockerfile”,rn “–context=gs://${GCS_BUCKET}/path/to/context.tar.gz”,rn “–${PROJECT}/${IMAGE_NAME}:${IMAGE_TAG}”,rn “–cache=true”]rn volumeMounts:rn – name: kaniko-secretrn mountPath: /secretrn env:rn – name: GOOGLE_APPLICATION_CREDENTIALSrn value: /secret/kaniko-secret.jsonrn restartPolicy: Neverrn volumes:rn – name: kaniko-secretrn secret:rn secretName: kaniko-secret’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eab96db8d50>)])]

The environment variable, GOOGLE_APPLICATION_CREDENTIALS contains the path to a Google Cloud credentials JSON file that is mounted at the path /secret inside the pod. It is through this service account key that the Kubernetes pod is able to access the build context files and push the image to GCR.

The downside to this method is you have live, non-expiring keys floating around with a constant risk of being leaked, stolen or accidentally committed to a public code repository.

3: Use Workload Identity

The third option usesWorkload Identity to provide the link between Google SA and Kubernetes SA. This grants the KSA the ability to act as the GSA when interacting with Google Cloud-native services and resources. This method still provides the granular access from IAM without requiring any service account keys to be generated and thus closing the gap.


You will need toenable Workload Identity on your GKE cluster as well asconfigure the metadata server for your node pool(s). You will also need a GSA (I called mine kaniko-wi-gsa) and assign it the proper roles it needs:

code_block[StructValue([(u’code’, u’gcloud projects add-iam-policy-binding ${PROJECT_ID} \rn –role roles/storage.admin \rn –member “serviceAccount:kaniko-wi-gsa@${PROJECT_ID}”‘), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eaba41bce10>)])]

On the Kubernetes side, create a KSA (I called mine kaniko-wi-ksa) and assign it the the following binding which will allow it to impersonate your GSA that has the permissions to access the Google Cloud services you need:

code_block[StructValue([(u’code’, u’gcloud iam service-accounts add-iam-policy-binding kaniko-wi-gsa@${PROJECT_ID} \rn –role roles/iam.workloadIdentityUser \rn –member “serviceAccount:${PROJECT_ID}[default/kaniko-wi-ksa]”‘), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eaba51b1610>)])]

The last thing you need to do is annotate your KSA with the full email of your GSA:

code_block[StructValue([(u’code’, u’kubectl annotate serviceaccount kaniko-wi-ksa \rn${PROJECT_ID}’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eab96d06cd0>)])]

Here is the pod manifest for the same image build job, but using Workload Identity instead:

code_block[StructValue([(u’code’, u’apiVersion: v1rnkind: Podrnmetadata:rn name: kaniko-wirnspec:rn containers:rn – name: kanikorn image: args: [“–dockerfile=Dockerfile”,rn “–context=gs://${GCS_BUCKET}/path/to/context.tar.gz”,rn “–${PROJECT_ID}/${IMAGE_NAME}:${IMAGE_TAG}”,rn “–cache=true”]rn restartPolicy: Neverrn serviceAccountName: kaniko-wi-ksarn nodeSelector:rn “true”‘), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eab96d06750>)])]

Although using Workload Identity requires a little more initial setup, you no longer need to generate or rotate any security account keys.

What if you want to access services in another Google Cloud project?

Sometimes you may want to push your images to a central container registry located in a Google Cloud project that is different from the one your GKE cluster is in. Can you still use Workload Identity in this case?

Absolutely! Your GSA and necessary IAM binding are created from your external Google Cloud project, but you still reference the Workload Identity pool and KSA your GKE workload is running from.

Now what

By using kaniko, we illustrated Workload Identity and how it allows more secure access when authenticating to Google APIs. Userecommended security practices to harden your GKE cluster and stop using node service accounts or exporting service account keys as Kubernetes secrets.

Source : Data Analytics Read More

Accelerate time to value with Google’s Data Cloud for your industry

Accelerate time to value with Google’s Data Cloud for your industry

Many data analytics practitioners today are interested in ways they can accelerate new scenarios and use cases to enable business outcomes and competitive advantage. As many enterprises look at rationalizing their data investments and modernizing their data analytics platform strategies, the prospect of migrating to a new cloud-first data platform like Google’s Data Cloud can be perceived as a risky and daunting task — not to mention the expense of the transition from redesign and remodeling of legacy data models in traditional data warehouse platforms to the refactoring of analytics dashboards and reporting for end users. The time and cost of this transition is not trivial. Many enterprises are looking for ways to deliver innovation at cloud speed without the time and costs of traditional replatforming where millions are spent on this type of transition. When access to all data within the enterprise and beyond is the future – it’s a big problem if you can’t leverage all of your data for its insights, and at cloud scale, because you’re stuck in the technologies and approaches of which aren’t designed to match your unique industry requirements. So, what is out there to address these challenges? 

Google’s Data Cloud for industries combines pre-built industry content, ecosystem integrations, and solution frameworks to accelerate your time to value. Google has developed a set of solutions and frameworks to address these issues as part of its latest offering called Google Cloud Cortex Framework, which is part of Google’s Data Cloud. Customers like Camanchaca accelerated build time for analytical models by 6x, and integrated Cortex content for improved supply chain and sustainability insights and saved 12,000 hours deploying 60 data models in less than 6 months. 

Accelerating time to value with Google Cloud Cortex Framework

Cortex Framework provides accelerators to simplify your cloud transition and data analytics journey in your industry. This blog explores some essentials you need to know about Cortex and how you can adopt and leverage its content to rapidly onramp your enterprise data from key applications such as SAP and Salesforce, along with data from Google, third-party, public and community data sets. Cortex is available today and it allows enterprises to accelerate time to value by providing endorsed connectors delivered by Google and our partners, reference architectures, ready to use data models and templates with BigQuery, Vertex AI examples, and an application layer that includes microservices templates for data sharing with BigQuery that developers can easily deploy, enhance, and make their own depending on the scope of their data analytics project or use case. Cortex content helps you get there faster — with lower time and complexity to implement. Let’s now explore some details of Cortex and how you can best take advantage of it with Google’s Data Cloud.   

First, Cortex is both a framework for data analytics and a set of deployable accelerators; the below image provides an overview of the essentials of Cortex Framework focusing on key areas of endorsed connectors, reference architectures, deployment templates, and innovative solution accelerators delivered by Google and our partners. We’ll explore each of these focus areas of Cortex in greater depth below.

Why Cortex Framework?  

Leading connectors: First, Cortex provides leading connectors delivered by Google and our partners. These connectors have been tested and validated to provide interoperability with Cortex data models in BigQuery, Google’s cloud-scale enterprise data warehouse. By taking the guesswork out of selecting which tooling works to integrate to Cortex with BigQuery, we’re taking the time, effort, and cost out of evaluating the various tooling available in the market. 

Deployment accelerators: Cortex provides a set of predefined deployable templates and content for enterprise use cases with SAP and Salesforce that include BigQuery data models, Looker dashboards, Vertex AI examples, and microservices templates for synchronous and asynchronous data sharing with surrounding applications. These accelerators are available free of charge today via Cortex Foundation and can easily be deployed in hours. The figure below provides an overview of Cortex Foundation and focus areas for templates and content available today:

Reference architectures: Cortex provides reference architectures for integrating with leading enterprise applications such as SAP and Salesforce as well as Google and third-party data sets and data providers. Reference architectures include blueprints for integration and deployment with BigQuery that are based on best practices for integration with Google’s Data Cloud and partner solutions based on real-world deployments. Examples include best practices and reference architectures for CDC (Change Data Capture) processing and BigQuery architecture and deployment best practices. 

The image below shows an example of reference architectures based on Cortex published best practices and options for CDC processing with Salesforce. You can take advantage of reference architectures such as this one today and benefit from these best practices to reduce the time, effort and cost of implementation based on what works and has been successful in real-world customer deployments.

Innovative solutions: Cortex Foundation includes support for various use cases and insights across a variety of data sources. For example, Cortex Demand Sensing is a solution accelerator offering leveraging Google Cloud Cortex Framework to deliver accelerated value to Consumer Packaged Goods (CPG) customers who are looking to infuse innovation into their Supply Chain Management and Demand Forecasting processes.

An accurate forecast is critical to reducing costs, and maximizing profitability. One gap for many CPG organizations is a near-term forecast that leverages all of the available information from various internal and external data sources to predict near-term changes in demand. As an enhanced view of demand materializes, CPG companies also need to manage and match demand and supply to identify near term changes in demand and their root cause, and then shape supply and demand to improve SLAs and increase profitability. 

Our approach shown below for Demand Sensing integrates SAP ERP and other data sets (e.g. Weather Trends, Demand Plan, etc) together with our Data Cloud solutions like BigQuery, Vertex AI and Looker to deliver extended insights and value to demand planners to improve the accuracy of demand predictions and help to defer cost and drive new revenue opportunities.

The ecosystem advantage

Building an ecosystem means connections with a diverse set of partners that accelerate your time to value. Google Cloud is excited to announce a range of new partner innovations that bring you more choice and optionality. 

Over 900 partnersput trust in BigQuery and Vertex AI to power their business by being part of the “Built with” Google Cloud initiative. These partners build their business on top of our data platform, enabling them to scale at high performance – both their technology and their business. 

In addition to this, more than 50 data platform partners offer fully validated integrations through our Google Cloud Ready – BigQuery initiative. 

A look ahead

Our solutions roadmap will target expansion of Cortex Foundation templates and content support for additional solutions in sales and marketing, supply chain, and expansion of use cases and models for finance. You will also see significant expansion with predefined BigQuery data models and content for Google Ads, Google Marketing Platform, and other cross-media platforms and applications and improvements with deployment experience and expansion into analytical accelerators that span across data sets and industries. If you would like to connect with us to share more details on what we are working on and our roadmap, we’re happy to engage with you! Please feel free to contact us at to learn more about the work we are doing and how we might help with your specific use cases or project. We’d love to hear from you!

Ready to start your journey?

With Cortex Framework, you come first in benefiting from our open source Data Foundation solutions content and packaged industry solutions content available on our Google Cloud Marketplace and Looker. The Cortex content is available free of charge so you can easily get started with your Google Data Cloud journey today!

Learn more about Google Cloud Cortex Framework and how you can accelerate business outcomes with less risk, complexity and cost. Cortex will help you get there faster with your enterprise data sources and establish a cloud-first data foundation with Google’s Data Cloud. 

Join the Data Cloud Summitto learn how customers like Richemont & Cartier use Cortex Framework to speed up time to value.

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AI is Driving Huge Changes in Omnichannel Marketing

AI is Driving Huge Changes in Omnichannel Marketing

Artificial intelligence is the latest trend shaping the omnichannel experience for customers in many retail outlets. One of the biggest trends pertains to personalization. The Forbes Research Council wrote an article in October citing research showing that 71% of customers now expect a personalized experience. The article makes the point that AI is key to meeting that expectation.

New AI technologies like text-based and in-app support are being used to improve digital experiences and customer service. Starbucks has an AI-powered app called My Starbucks Barista that allows mobile users to order from a virtual barista within the app. The order is then sent to a nearby store to avoid long lines. Retail and other industries are using omnichannel and AI technology to improve their services.

AI has many practical uses that can help companies improve their marketing strategies, but personalization is arguably one of the most important. AI is essential for scaling end-to-end personalization. In omnichannel marketing, AI personalizes and optimizes the customer experience across multiple channels. It analyzes data about customers and products to inform marketing campaigns, predict conversion channels, and automate repetitive tasks. Omnichannel marketing automation powered by AI enables end-to-end personalization at scale.

Bloomreach author Donna-Marie Bohan cites many case studies on the benefits of using AI to improve personalization:

A cosmetic company increased revenue by 50% by using AI to improve their email campaigns.

bimago increased online conversions by 44% by using AI to better optimize their web presence.

AI is going to be vital to the future of omnichannel marketing, especially as more customers demand a personalized experience.

AI is the Key to Improving Personalization for Omnichannel Marketing

In the digital era, customers expect seamless and highly personalized shopping experiences across all channels. AI technology helps companies meet those expectations.

Businesses must keep up with ever-changing consumer preferences by addressing their customers’ needs. One solution to this challenge is omnichannel e-commerce, a customer-focused, AI-driven strategy that aims to provide a seamless shopping experience across multiple channels.

Omnichannel software uses AI technology to help businesses deliver personalized and seamless shopping experiences that accommodate customer preferences. Companies can improve sales while attracting new shoppers and boosting satisfaction. Brands like Mitto, a leading provider of global omnichannel communications solutions, help businesses integrate omnichannel solutions and personalize the customer’s experience with the power of some of the latest machine learning algorithms.

With omnichannel strategies, companies can integrate consumer information from all linked channels — social media, email, text messages, and others. This gives businesses a clear picture of the customer’s journey and preferences. Also, with client data at hand, companies can provide personalized products and services to consumers based on their previous shopping experiences. Firms that supply customer-centric services also benefit from increased sales and customer growth.

Another reason businesses adopt omnichannel e-commerce strategies is that AI technology can help them improve operational efficiency and reduce costs. Companies can provide consistent brand experiences, enabling them to boost customer loyalty and trust, enhancing a brand’s reputation while reinforcing its values and messaging.

Customer service is another crucial factor for businesses to consider when improving their services and working to retain clients. Omnichannel customer service teams can use AI to help companies provide personalized support by addressing consumers’ concerns or questions more efficiently and quickly. With this type of customer service, businesses can increase satisfaction while gaining the benefits of greater customer retention and improved sales.

Omnichannel e-commerce methods help organizations with supply chain management by ensuring real-time visibility of inventory levels across all channels, which enables firms to efficiently control their stock levels and minimize the likelihood of shortages or overstocking. Furthermore, omnichannel methods can assist retailers in making intelligent product positioning and advertising decisions based on consumer behavior and demand trends.

AI platforms like Mitto, a leading provider of global omnichannel messaging solutions, can help businesses integrate these systems while personalizing clients’ journeys and experiences. Mitto offers solutions that empower companies to easily communicate with their customers through multiple platforms, including voice, SMS, chat apps, and social media, allowing companies to reach customers on their channels of choice and provide personalized experiences.

Moreover, its solutions can be integrated with existing business systems such as customer relationship management and e-commerce platforms. This enables businesses to access consumer data, including purchase histories and preferences, across all channels and provide personalized communication. Thanks to rapid technological development, platforms like Mitto use advanced data analytics techniques and state-of-the-art machine learning algorithms to help businesses offer customized services and products by understanding customers’ shopping behavior and needs.

Understanding Customer Preferences

Nowadays, businesses have evolved by shifting some of their operations online to e-commerce. Companies have been forced to keep up with changing customer needs by learning preferences in an effort to provide suitable personalized services and products.

Customers are generally unique, with their own preferences and needs. Therefore, businesses must improve their strategies by addressing consumer preferences. Customers are more satisfied when their needs are met. This can help build a company’s reputation, retain customers, and improve sales. As buyers make purchases, businesses collect enough data to help them understand customers’ needs and expectations. Omnichannel software can help companies analyze consumer data and deliver more personalized services.

Whether a business sells products or offers services, it’s essential that it understands its clients’ preferences. When buyers’ preferences are fully understood, businesses can improve customer management while increasing sales. Integrating omnichannel strategies into business operations makes this easier to achieve.

Seamless Shopping Experience

Customers are currently more focused on experiences than purchasing processes or the service being offered. With seamless shopping, buyers expect an uninterrupted and smooth journey across multiple channels, such as online and in-store. This is a crucial step that puts customers at the center of every interaction, and businesses need to move forward by understanding this important concept in the e-commerce industry.

In order to give their clients a seamless shopping experience, companies will need to use an omnichannel approach to collect customer data and locations. An enterprise can then merge those data points with past consumer data and analyze them to identify customer patterns while predicting future behavior.

Omnichannel solutions help companies improve engagement rates by ensuring a consistent message across multiple channels, increasing brand awareness and enhancing the customer journey.

Omnichannel Marketing

Omnichannel marketing has proved to be a valuable strategy that businesses can implement to meet their customers regardless of demographics and provide consistent brand experiences throughout the channels. This approach is focused on delivering a seamless experience across several platforms, including social media posts, in-store experiences, and company websites.

With omnichannel marketing, businesses can collect and analyze customer data from multiple channels to understand consumers’ preferences, purchasing patterns, and behaviors. Companies need information to create personalized marketing strategies that meet customer needs and expectations. Omnichannel marketing consists of multiple strategies and techniques, including targeted advertising and email marketing.

A platform like Mitto can assist businesses with their omnichannel marketing strategies by providing many integrated solutions that allow companies to communicate efficiently with their customers through multiple channels. An omnichannel provider enhances marketing in different ways.

Communication is key for any business that must inform customers about its offerings. Omnichannel communication can help companies communicate with customers through several channels while delivering personalized customer experiences.

Businesses collect more of their customers’ data than they realize. Mitto can help firms gain an advantage by collecting and analyzing data to provide a customized experience. This enables companies to use omnichannel marketing by targeting a particular customer group based on their preferences.

Personalized Customer Service

As e-commerce has evolved over the years, every business needs to prioritize delivering personalized customer service. Customized service ensures that companies provide clients with support and assistance based on their needs and preferences. Also, personalized service can help a business build solid relationships with its customer base, thus increasing satisfaction and loyalty, which will, in turn, improve sales and profitability.

Businesses can deliver personalized customer service by collecting and leveraging customer data and incorporating it into their AI models. As companies collect consumer data, they can learn buyers’ habits and purchase behaviors to tailor communications to customers’ specific interests. Businesses can easily offer unique, tailor-made solutions to customers after learning their preferences and expectations.

Potential clients want to easily access products and services across multiple platforms. Companies should make it simple for customers to do so by integrating AI-driven omnichannel e-commerce strategies that allow people to access their profiles regardless of the channel.

Omnichannel messaging can help industries provide more personalized customer service by reaching consumers through platforms of their choice. Omnichannel messaging also integrates customer information across several channels, providing industries with a detailed view of their customers’ needs and expectations.

AI Improves Personalization for Omnichannel Marketing

AI technology has drastically changed the direction of marketing. Its impact on omnichannel marketing is even more significant. A growing number of companies are using AI to personalize the experience of customers across various mediums and provide a uniform experience. Smart marketers will leverage AI to make personalization a key part of their strategy.

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Key Strategies to Develop AI Software Cost-Effectively

Key Strategies to Develop AI Software Cost-Effectively

You have probably read a lot about the major changes brought on by AI technology in recent months. Many people have called 2023 “the year of AI.”

Every day we hear new stories about miraculous breakthroughs caused by ChatGPT and other AI applications. For example, ChatGPT recently The Independent said that the technology is starting to show signs that it can think like humans.

Some companies are interested in taking advantage of the benefits of existing AI tools like ChatGPT. However, others want more control over AI technology, so they are seeking to develop their own AI software. The ROI of creating their own AI applications can be massive, but they still need to use them cost-effectively.

Developing AI Software Can Help Many Companies Develop a Competitive Edge

Software development services can be very beneficial for companies trying to take advantage of the benefits of AI technology. In the current economic situation, many companies have seen their budgets shrinking, but the need for software development remains steady.

A study done by Deloitte in late 2022 showed that cost reduction is the top priority for CFOs. Over half of the CFOs included in the study (55%) said that cost reduction is now a strong priority. AI technology can help them cut costs significantly, but they have to invest in it wisely. If you are aiming to cut down your skyward development costs without compromising quality, this article outlines some ways you can consider approaching this goal. 

Consider Outsourcing

One of the biggest ways to reduce AI software development costs is by outsourcing. Partnering with a software agency can allow you to use their huge pools of resources and expertise. By outsourcing, you remove any geographical limitations, allowing you to work with the best agencies and individuals all over the world

Agencies in countries with lower labor costs will be able to provide you with AI software development services at a small percentage of the cost. Places like Eastern Europe and India are extremely tech-savvy, yet their agencies charge significantly lower fees than their European and North American counterparts. 

However, be sure to spend some time vetting potential partners, as communication barriers could stall your project. Be sure that you and your agency have a language in common before you commit to anything. You also want to make sure they have a background with Agile, since Agile is crucial for developing AI software.

Avoid overspending or borrowing money in order to cover your software development. You may find that you have to keep topping up on your budget for extra developer hours or added features and the last thing you want is to have a high cost or bad credit loan hanging over your head.

Hire Contractors For Small Jobs

Some specific parts of your project might be better handled by contractors than agencies or full-time employees. You can hire certain talent or contractors to work for specific parts of your project. Since you contract them directly, there are no agency fees.

Contractors can be a more cost-efficient alternative to hiring full-time employees. You only pay for the work that is completed, as contractors are paid on a project basis. This means you can avoid the overhead cost associated with full-time employees including pensions, sick pay, holiday pay, and training. 

Version 1, Version 2

This refers to starting with a basic version of your product and then improving upon it in subsequent releases, adding features and improving its design. This allows you to save money on software development, but also allows incremental development, which can be easier on your pockets. It allows you to reuse existing code instead of starting from scratch each time.

It also has the added bonus of getting your product to market faster, allowing you to generate revenue sooner. You can also use it as an opportunity to gather feedback on the basic version of a product to ensure that you are spending money developing a product that people actually want and need, instead of making assumptions. 

Provide Detailed Requirements

Whether you are outsourcing or using your own in-house team, make sure you communicate your requirements as clearly as possible. This includes being clear about the scope, objectives, and function of your project.

This means you will not need to spend too much time on back and forth interactions. Clear goals allow your team to work efficiently and without excessive resource expenditure. You will therefore not need to bear the cost of additional working hours.

Use Prebuilt Features

If your goal is to save money, see if you can use templates and prebuilt features to create new AI software. This can save your development team a lot of time, as they will not have to write code from scratch.

Prebuilt features and templates will have already been performance tested, and they typically come at much lower price points than developing a product from scratch. This not only saves you money but time.

You can use as few or as many prebuilt features as suits you. For example, you might want your own custom website built from scratch, but you can still rely on prebuilt structures for widgets, components, and add-ons. There are plenty of high quality options that can be seamlessly integrated and customized.

Quality Assurance Specialist

If possible, try to involve a quality assurance specialist in the development of your AI application. This specialist will analyse your software throughout each stage of development to keep your final product free of bugs and errors. Although this can seem like an unnecessary inconvenience, it will save a lot of time and money in the long run.

If you wait until the last moment to test the quality of your product, you may have accumulated such a large number of errors that going back to the drawing board will cost you more money than hiring a quality assurance specialist. 

Automate Your Testing

AI technology can also help create other AI applications. One of the benefits is that it can help with automating coding and testing.

Automated testing refers to the process of executing your software tests without manual intervention. Manual testing requires time, effort, and money. By automating testing, your company might see improved speed and consistency.

Automations reduce the risk of human error, leading to higher accuracy and reliability. This means you run less of a risk of bugs or other issues, which can easily escape the notice of a human. Spotting these issues early will save you money later down the line.

Automated tests can be set up so that they run each time the code is tweaked. This means bugs can be spotted in the development process as soon as they arise. Fixing an error as soon as it crops up can be far cheaper and easier than trying to fix them later in the development cycle.

Finally, automated tests are reusable. Once you create a test, it can be run repeatedly without needing to be modified. 

Reuse Code

Using existing code in a new project can be a cost-effective way to get projects finished faster. By reusing code, rather than writing new code from scratch, your team can save time and money. Reusing code speeds up the development process by allowing developers to focus on customizing and improving existing code.

Another benefit of reusing code is standardization. Reused code leads to more consistent code that has already been tested and reviewed. Standardization also makes it easier to maintain the software over time, thereby reducing the risk of compatibility. This means you can spend less money and energy fixing issues later down the line.

Find Cost-Effective Ways to Develop AI Software

AI software can be very helpful for companies looking to cut costs. However, they still need to make sure they use cost-effective strategies to develop it. The ideas listed above can be very helpful.

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Maximize Tax Deductions as a Business Owner with AI

Maximize Tax Deductions as a Business Owner with AI

AI technology offers a number of major benefits of small businesses and freelancers. The market for AI is projected to be worth nearly $1.6 trillion by 2030.

We have talked about how businesses are using AI for marketing and tools like ChatGPT to create content more easily. However, one of the other benefits of AI is that it can help with your taxes! This is important, since taxes are a huge pain for many business owners.

If you make $400 or more as a freelancer or small business owner, you must file taxes with the  Internal Revenue Service (IRS). Self-employed taxpayers can register their business as a sole proprietorship, partnership, S Corporation, or LLC. C Corporations are not allowed to file taxes as freelancers or self-employed entities.

So, why not use new AI applications like the newest versions of TurboTax to assist with your taxes? This article will help you figure out what you should focus on and how AI tools can help.

How Can You Use AI to Maximize Your Tax Deductions as a Freelancer/Small Business Owner?

Freelancers and small business owners are self-employed and must manage their taxes and file with the IRS. If you are looking to maximize tax deductions, the IRS provides several ways to take advantage of legal methods.

We mentioned that the IRS is using AI to help fight tax evasion. However, businesses can also use AI to help manage their own taxes more easily.

Accounting and tax issues can be costly for businesses. However, artificial intelligence can help with their accounting needs, whether it’s a shared service center or a local bank. Advanced AI algorithms can reduce costs, save time, and improve ROI. It can also increase productivity and provide new information to support decision-making.

Before you know the eligible tax deductions, here are a few quick tips for planning tax filing as a freelancer or small business owner.

Keep your personal and business financial records separate.

Open a separate bank account/credit card for your business use.

Maintain accurate books for your business income and expense transactions.

Know the percentages/pro rata portions of business and personal expenses.

Know the proper entity structure for tax deductions as a self-employed freelancer or business owner.

Let’s discuss some useful tax deductions for your small business or freelancing business.

1.      Use AI to Deduct the Right Startup Costs

The IRS allows you to deduct certain expenses as startup business costs against any business loans or money you have raised.

“Common startup expenses include potential market research,” explains Jane Moore of money site, Loanza, “you also have surveys, initial travel costs, advertising for opening, salaries or wages paid to employees initially, and salaries paid to executives or consultants.”

Make sure not to include capital business expenses like purchasing property, vehicle, or business equipment” she continues – “ as startup costs, these are all capital expenditures.”

TurboTax now uses artificial intelligence to help customers get their highest possible refund. It recommends whether or not they would like to go through the itemized deduction process or not, saving users up to 40% of tax prep time. For the customers that do choose to itemize their taxes, TurboTax uses data analysis and machine learning to identify and recommend deductions, including obscure ones.

2.      Deductions for Office Supplies and Tools

Small business owners can deduct the cost of goods sold (COGS) if they produce and sell products. Similarly, freelancers can deduct office supplies and tools like paper, ink, stationery items, books, note papers, pens, etc.

You can also deduct the cost of your AI-driven tax preparation software. Of course, you want to find a service that uses the right AI algorithms to assist with taxes.

The company also applies artificial intelligence technology to product development, speeding up the process of getting tax software to market each year with the latest tax codes and maximum accuracy. Artificial intelligence and machine learning simplify the process for customers by converting the complex 80,000 pages of the U.S. Federal tax code into an application.

3.      Professional Membership and Legal Fees

Freelancers often have to pay a membership fee to provide services on a professional platform. You can deduct this fee to maximize your tax deductions.

Similarly, if you paid any legal or consultation fees to an attorney or business consultant, you can deduct it from your gross business income.

4.      Home Office Business Deductions

Small business owners or freelancers work from home normally. The IRS allows you to deduct a portion of home expenses as your business costs.

You can use the dollar rate of $5 per square foot used for business activities or calculate the total area of your house and then find the proportional use for your business.

5.      Self-Employed Tax Deductions

Self-employed must file their social security and medicare expenses as an employer and employee. They must file for the employer portion of self-employed taxes as well.

The social security tax rate is 12.4% and medicare is 2.9% for a total of 15.3%. You can then deduct half (7.65%) of this tax from your business income to reduce your tax liability.

6.      Health and Business Insurance Deductions

If you are reluctant to pay for your family or business insurance plans, consider them as a business expense with applicable limits.

You can deduct your health insurance contributions for yourself, your spouse, and your dependent kids. Similarly, you can lower the tax bill by deducting the business insurance costs.

7.      Utilities and Internet Expenses

A common way to maximize tax deductions is to include utilities like electricity, water, and phone service costs from business income.

You can also deduct the internet subscriptions used for business purposes.

8.      Vehicle and Travel Expense Deductions

Buying or leasing a vehicle means it’s a capital expenditure. You can depreciate the total cost of a vehicle over its useful life by using one of the allowed methods by the IRS.

Similarly, you can deduct business travel expenses by following the instructions of the IRS.

9.      Maximize Your Retirement Plan Contributions

If you make sufficient money, contributing to your retirement plans as a freelancer or small business is the best way to maximize tax deductions.

Contribution limits vary by a retirement plan and change often. For instance, the contribution limit for a solo 401(k) plan for 2023 is $66,000 plus the catch-up amount.

10.  Other Important Tax Deductions to Know

Here are a few other useful tax deductions you should know.

The cost of designing, hosting, and maintaining a business website.

Advertising & Marketing costs of your business.

Professional and Accounting Costs paid for your business.

Financing Costs like interest paid on a business loan.

Subscription or purchase costs for Software and Online tools.

You can deduct eligible Charitable Contributions from your business income.

Business Meals and Refreshments costs for business guests can be deducted up to certain limits.

Use AI Technology to Make Tax Planning Easier as a Business Owner

AI technology can be a huge boon for many business owners. One of the underappreciated benefits is that it can make tax planning easier. You want to take advantage of the benefits it provides.

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Effective strategies to closing the data-value gap

Effective strategies to closing the data-value gap

We have been going through a phenomenal amount of technological advances in the space of big data and cloud, driven by the organizations’ needs to get value out of data. It’s a given that data drives innovation, but business needs are changing more rapidly than processes can accommodate, resulting in a widening gap between data and value. Luckily, there are proven strategies that organizations can utilize to close the data-value gap. According to McKinsey, top performing organizations who see the highest returns from Artificial Intelligence (AI) are more likely to follow strategy, data, models, tools, technology and talent best practices. 

Our new Modern Data Strategy paper focuses on aligning data experiences, data economy and data ecosystems as part of the decision making process so that every organization can maximize ROI from their data and AI investments.

Figure: the three key areas of consideration to close a data-value gap

Data experiences to leave no user behind

Data experiences are key for organizations to not just democratize the reach of data and AI but also enable organizations to get the most out of their people. For example, a leading social networking service allowed a whole new group of less technically inclined users, including data analysts and product managers, to get insights from data using Google Cloud tools such as BigQuery and Looker. Users can access the data they need with self-service, adding agility across the business.

According to IDC, by 2026, 7 PB of data will be generated per second globally. At the same time, only 10% of the data generated each year is original, while the remaining 90% is replicated. This is because the organizational culture isn’t changing together with the new capabilities — this is holding back the outcomes and results that organizations expect. Users don’t have access to useful data experiences that match their maturity level.

To help solve these challenges, the Modern Data Strategy paper suggests different methods you can implement to help build personalized and self-service data experiences, giving everyone a chance to make data-informed decisions.

Data economy to capitalize on the value of data

Even though data platforms have evolved, the organizational model for becoming data driven, making data accessible and using it effectively has not evolved. Organizations and ways of working are trying to keep up with this rapid change to close the data-value gap. Applying a DevOps mentality to data helps closing this gap. Managing data as a product with clear ownership and SLAs allows organizations to get more value out of it.

Figure: The data economy potential is still in the early stages

Data consumers at Delivery Hero used considerable time figuring out where the data was, how to access it, and understanding the quality and policies around usage and sharing. This was due to a fragmented setup where teams were building their own data silos. Delivery Hero developed their new data platform on Google Cloud using data product thinking and settled on a global catalog that documents all available data and its meaning, quality and source. Teams discover, use and build on datasets from other teams with ease and speed, reducing the time to access data from 48 hours to having access to live data at all times. This has helped teams develop models to deliver growth and value across the business, from route management for drivers and order predictions in logistics to better recommendations and personalization on the website.

Data ecosystem to foster innovation

Closing the data-value gap can have a huge impact on an organization’s competitiveness. According to IDC, by 2025, at least 90% of new enterprise application releases will include embedded AI functionality. From a technology perspective, data platforms support these ambitions already. Choosing the right data ecosystem can improve the scalability, usability and time to insight of your analytical teams.

A modern data ecosystem is key to organizations becoming more effective and efficient. BigQuery is at the heart of Google Cloud’s analytics lakehouse with a strong ecosystem of tools around it. By having such an open, unified and intelligent platform, your teams do not need to spend time reinventing the wheel or spending time in data plumbing tuning underlying infrastructure.

For example, Mercado Libre was able to build a new solution that provided near real-time data monitoring and analytics for their transportation network and enabled data analysts to create, embed, and deliver valuable insights. This solution prevented them from having to maintain multiple tools and featured a more scalable architecture. 

Turn data into a competitive advantage with the right strategies

Data is the heart of digital transformation and offers incredible opportunities for organizations to accelerate the most strategic business outcomes, such as:

increasing revenue by understanding customer preferences and offering personalized experiences,

enhancing workforce productivity by making data-derived insights easily accessible to each worker to foster data-informed decision making.

Primarily, organizations need to be guided by a robust data strategy. This in turn will enable them to get value from data and turn it into competitive advantage. The bottom line is that data-driven companies innovate faster. First, they are able to continuously optimize their operational efficiencies regardless of the size and complexity of their organizations, and as a result they keep costs down. Second, they can adapt quicker as market conditions change. 

Here are four potential ways to drive new value using data: 

Applications: Accelerated product development and shorter time to market

Analytics: Greater organizational and operational efficiency, agility, and pace to execute innovative programs. 

Visualizations: Increased productivity by using right intuitive tools to allow doing advanced analytics and AI.

Predictions: Creating differentiated solutions driven by Data and AI

Benefits of your data strategy

A data strategy helps you create the necessary alignment across your organization.

These are some examples of activities that your data strategy should drive:

Principles and processes to guide the organization toward faster decision-making and continued alignment with business goals and objectives

Continuous review of recommended systems and tools that align with your strategy’s vision while avoiding a one-size-fits-all approach

Clear and consistent policies and procedures for managing data securely throughout its lifecycle, from creation to disposal

Ensuring that data is used ethically and responsibly, in compliance with relevant laws and regulations

Creating and enabling a culture of data-driven decision-making through the use of accessible, governed data to drive business value

The responsible use of AI across your organization

A framework for building a modern data strategy

Organizations can follow the three pillars below for building a modern data strategy:

Data experiences: Productive user experiences enabling all users to access and create value from relevant data

Establish a data university to advance data literacy for everyone in your organization

Pivot your organization from role-oriented to product-oriented with cross-functional teams

Define data principles for your organization that align with priorities and provide clarity in decision making

Data economy: Principles and practices to ensure that data can be published, discovered, built on, and relied on

Staff a data platform team that is obsessed with the developer and analyst experience

Make sure you get external/enterprise source data into the data economy early, such as customers, transactions, and product data

Find teams that depend on each other for data and help them share their data as a data product

Implement basic data governance practices — stewardship, discovery, quality, reliability, and privacy 

Data ecosystem: A unified, open and intelligent platform with end-to-end data capabilities for all users and needs

Choose a data ecosystem that provides your organization with all the capabilities you need out of the box, with open standards to plug in other components where needed

Make a plan to enable everyone in the organization to apply AI in their work using the capabilities in the ecosystem

Keep your operational work to a minimum by choosing serverless tools for the capabilities in your data ecosystem

Getting started

These are some important questions to ask yourself before designing a modern data strategy for your organization:

Does everyone in the organization have positive experiences working with data?

Do you have dedicated learning paths for different personas in your organization to use data effectively?

Do you have democratized access to data products to build the data economy?

Does it support all your data people, including app developers, business analysts, data scientists and line of business users?

Can you enable the next generation of data driven solutions including Data Science / Machine Learning (ML) at scale?

Are you effectively using data and AI to fast-track your strategic business objectives?

Can your data platform provide the technological ecosystem that enables data processing at scale with modern tools?

Can you share data internally and externally in a governed way?

Read our new paper, Three pillars for building a modern data strategy, to find out how to get started and learn more about how to define a modern data strategy for your business.

It was an honor and privilege to work on this with Thomas de Lazzari, Sina Nek Akhtar, David Montag, Andreas Ribbrock, Zara Wells for support, work they have done and discussions.

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