Using Data-Driven Lean Thinking to Optimize Business Processes

Using Data-Driven Lean Thinking to Optimize Business Processes

Data-driven decision-making has become a major element of modern business. A growing number of businesses use big data technology to optimize efficiency. However, companies that have a formal data strategy are still in the minority. Only 32% of executives have officially laid out a data strategy to drive their organization.

Furthermore, only 13% of companies are actually delivering on their data strategy. Part of the reason is that they focus almost entirely on the technology itself without a clear plan for leveraging it effectively.

One of the ways that smart business leaders can create successful data strategies is to utilize lean thinking. Keep reading to learn how to combine these two initiatives.

Lean Thinking and Big Data Can Be Merged for Superior Decision-Making

For CEOs and company managers, it is crucial not to waste time and resources on tasks that do not make a significant contribution to the company, but instead are a waste. However, thanks to contributions from the Professional Master’s Degree Executive MBA area, systems have been created that minimize waste and at the same time maximize useful and beneficial results.

It is a lot easier to get the most bang for your buck with time and financial resources if you know how to take advantage of data analytics tools. A lot of analytics tools make it easier to monitor the performance of various strategies, so companies can use their resources more wisely.

The insights derived from data analytics tools allows them to avoid wasting time on tasks that don’t pay real dividends. This illustrates the benefits of combining big data and lean thinking.

While there are various interpretations or models to address such problems, Lean Thinking can contribute to the implementation of more optimal projects for a business. Keep reading to learn more about lean thinking and we will follow up on the ways to combine it with data analytics below.

What is Lean Thinking?

Creating value and improving efficiency and productivity at work, in order to reduce costs and waste is what Lean Thinking methodology is all about, i.e., it means that everything that is only inoperative for a business is set aside, leaving processes that have as objective the continuous improvement.

With the purpose of achieving contributions that represent greater value using fewer resources and time, a series of methods and tools are applied that allows the company to manage in a more effective way. Increasing the performance of the budget and the efficiency of automatic operations, and thus, increasing the production process, delivering quality products and services to the clients in the minimum amount of time possible.  

Implementing Lean methodology

Within the Lean Thinking methodology, there are five essential components:

Defining value: The value of the product will be defined by the number of customers who are interested in acquiring it, therefore, there must be a balance between price and the work behind the product or service, as well as satisfying the customer’s need.    Determining the Value Stream: To identify where the points are generating value and what actions are merely wasteful; so, maintaining production with only the necessary use of resources and without sacrificing quality must be present.

Creating a map, a scheme or a software will allow for quicker identification of this. 

Creating flow: The flow of all remaining stages of the life cycle should be executed as fluid as possible. Therefore, it must be verified which sequences add value and which are dispensable.Establishing pull: It minimizes inventory and work-in-progress items as much as possible. Everything produced and spent and the amount of product generated should be in accordance with the customer’s demands, which ensures that production is streamlined and lean.Pursuing perfection: Excellence is paramount, which should not be lost sight of, and therefore, if continuous evaluation is relevant in various steps, such as waste generation and looking for opportunities for advancement in order to have continuous improvement, this should be done, and thus, achieve efficient results, depending on the trends and how this affects business.

Advantages in applying Lean Thinking

Finally, applying this methodology brings with it a host of advantages for companies:

By bringing products to market in the shortest possible time, more resources and time are available to improve the product, thus meeting the needs and expectations of the public and strengthening loyalty.By having a visualisation of what generates profits and losses, the risk is lower because decisions are based on data and not assumptions. These decisions are better informed and therefore less prone to error.There is an increase in commercial workload which means immediate availability of products, as well as a reduction in defective items or downtime, minimising waste and overproduction, saving inventory management. All employees have the opportunity to participate in the whole process and the failure rate is reduced. This influences the motivation of the team and, consequently, their productivity. In addition, there will be a better working atmosphere, which is essential for the smooth running and flow of daily tasks.  

Lean Thinking will not only save time and money, on the other hand, this saving can be derived to other sectors or personnel that need improvement, therefore, investing resources in other aspects will allow the most favorable and rapid development of the company.

How Can You Use Lean Thinking with Big Data?

There are also a number of ways that you can use big data technology to improve your lean decision-making strategies. Some of the options are listed below.

Determine the Types of Products that Actually Sell

You have probably had times when you spent weeks developing new products for your organization, only to witness that nobody ever wanted to purchase them. The good news is that data analytics tools help companies monitor sales of different products, so they can focus on developing those that are likely to sell. Many data-driven companies are using QR codes to track sales. This data doesn’t just help with inventory management. It also helps companies invest their R&D resources wisely and keeps them from wasting energy thinking of product ideas that will never sell.

Identify Tasks that Don’t Translate to Higher Productivity

You will also want to make sure that the tasks you and your employees are working on will actually lead to higher productivity. A number of analytics tools like GetApp and help companies assess the tasks that employees are performing and determine the value they are adding to the organization.

Monitor Markets for Profitability

Many companies spend a lot of time and resources brainstorming strategies to scale into new markets. They might want to conquer the markets of new countries or expand into new demographics.

Data analytics technology is able to help them determine the profitability of the markets that they have focused on so far. They can use this data to decide which markets to ignore, so they don’t waste time thinking of new strategies that might not lead to higher profits.

Lead Decision Thinking and Big Data Are Invaluable to Modern Businesses

Ninety-one percent of companies feel that big data is important for decision-making, but only around a third of those companies actually have a data strategy. If you want to stand out among competitors, you will want to implement a data strategy that embraces the benefits of lean thinking.

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5 Reasons SoD Protocols Are Vital to Modern Data Security

5 Reasons SoD Protocols Are Vital to Modern Data Security

Data breaches are becoming far more common these days. Security Magazine reports that over 22 billion records were exposed in the over 4,000 publicly disclosed data breaches last year. The actual number is likely higher, since many data breaches are never reported.

We have talked extensively about the importance of taking precautions to prevent data breaches. Most of our recommendations have focused on the use of technology such as VPNs to thwart cybercriminals trying to steal data. However, there are a lot of other organizational safeguards to prevent data breaches that get less attention.

One strategy for preventing data breaches is to utilize “separation of duties.” You may have heard of the term “separation of duties” before, but what does it mean, and why is it so crucial for businesses trying to protect their data? In this article, we’ll go over five reasons why separation of duties is essential for any company and provide tips on implementing it successfully.

What Is SoD?

Separation of duties is a security practice that helps to prevent fraud and errors by ensuring that no one individual has too much control over a process. The logic behind SoD is that it is more difficult for one person to complete a task successfully if they are not allowed to perform all of the steps. For example, in a financial transaction, the person who initiates the transaction should not also be the one who approves it.

This might seem like it could lead to an unnecessary bureaucracy, but it can actually help mitigate the risks of a data breach. By ensuring that different people are responsible for various aspects of a process, separation of duties makes it harder for someone to commit fraud or make an error without being detected. Since at least 34% of data breaches are internal, these SoD safeguards can be very important.

In many organizations, separation of duties is accomplished through role-based access controls, which restrict users to only those tasks that are appropriate for their role. For example, a cashier might only be able to initiate transactions, while a manager would be able to approve them. Separation of duties is an important security measure that can help to improve the accuracy and integrity of organizational processes.

The Benefits of Using SoD in the Workplace to Prevent Data Breaches and Fraud

There are many benefits to implementing SoD in the workplace. Here are some reasons these protocols are effective at stopping data breaches and fraud.

Prevent Fraud

Perhaps the most crucial benefit is that it can help to prevent errors and fraud. Ensuring that different people are responsible for various aspects of a process, SoD makes it more difficult for someone to commit fraud or make an error without being detected.

Fraud can play it in many different ways. Employees may deliberately steal and sell data, which places the company at risk. Deliberately conducted data breaches are a huge threat to organizations in the digital age.


Another essential benefit of SoD is that it can help to improve efficiency. When different people are responsible for other tasks, they can specialize in those tasks and become more efficient at them. This can lead to overall improvements in the efficiency of the organization’s processes.


SoD can also improve accountability within an organization. When different people are responsible for other parts of a process, it is easier to identify who is responsible for any errors or problems. This can lead to improved accountability and a better understanding of the organization’s processes.

Improved morale

SoD can also lead to improved morale within an organization. When employees feel accountable for their work and that it is essential to the organization, they are more likely to be motivated and have a positive attitude.


Finally, SoD can help to create a culture of safety in the workplace. By ensuring that no individual has too much control over a process, SoD helps create an environment where people are less likely to take risks that could lead to errors or accidents. One of the biggest risks companies face is compromising their data. SoD can be very effective at preventing this.

How to Start Implementing SoD in Your Business

If you’re interested in implementing SoD in your business, there are a few things you need to do to get started. First, you need to identify the critical processes in your organization that would benefit from SoD. These are typically processes that involve financial transactions or sensitive data. Once you’ve identified these vital processes, you need to determine what tasks need to be separated and who will be responsible for each job.

Next, you must put the necessary controls to ensure adequate separation of duties. This might involve implementing role-based access controls or creating new policies and procedures. Finally, you need to train your employees on the new SoD requirements and make sure they understand how to comply with them.

SoD is Essential for Preventing Data Breaches and Fraud

Implementing SoD can be a bit of a challenge, but it’s well worth the effort. You can’t take the risk of a data breach these days, which SoD protocols can help prevent. By taking these steps, you can help prevent errors and fraud while improving efficiency and accountability within your organization.

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5 Vital Business Intelligence Tips All Companies Should Embrace

5 Vital Business Intelligence Tips All Companies Should Embrace

Business intelligence is an integral part of any business strategy. It helps to turn your data or objectives into something meaningful.

Business intelligence software can integrate information and present it in dashboards, reports, or graphs. Sixty-four percent of BI users have felt it was very helpful.

It is also essential for a business to have a bi consultant who helps the business enhance its data strategy and processes. Are you looking for power bi consulting services? Then there are many sites that offer these services, like Below are the best helpful business intelligence tips that you should be aware of.

Keep Your Eyes on the Higher perspective

Business intelligence has become very important in our data-driven economy. However, you have to know how to leverage it effectively.

You might have a prompt issue that you want to address and need to utilize BI to arrive. This might fill in as a momentary arrangement. In any case, you may likewise find that you made a significant investment resolving one issue only to find that it will not help with future issues.

Part of your BI procedure should be trying to zero in on arrangements that can help you respond to many inquiries over the long run. Your BI ought to be versatile as your organization develops and objectives change.

Your BI ought to line up with your general business goals. Whether you are hoping to develop activities further, increase deals, or offer more productive support to your clients, your BI investigation should have the option of identifying the most pertinent information for your objectives.

Get Insights from the Right Stakeholders

Regardless of your business insight group size, you want to include a sample of views from relevant stakeholders that can help you incorporate changes. Your group ought to incorporate a few jobs:

Individuals who are seasoned veterans that utilize instruments to help you recover and show informationIndividuals with the right experiences to decipher the informationIndividuals who can uphold the association by trying the information

Your association might require you to make preparations across different offices. Your business staff needs to understand that the information will help them with making better business choices. If you identify the biggest benefits between your business staff and your BI group, you will have the biggest benefits from discovering important business insights. This will be contingent upon the size of your organization. A portion of these jobs might be covered with these changes. You may also find that you want to work with an external business intelligence advisor to find the best direction to take.

Have a Dexterous Mentality

A lithe outlook can help with separating a huge venture into more modest, sensible expectations. Instead of framing a BI technique that you can try to carry out in a few months, you would be better off trying to adopt the strategy of “What might we at any point achieve in the following 2-4 weeks?” These short augmentations are frequently alluded to as “runs.” In a light-footed project, the board philosophy takes consistent criticism from your business staff into account. If one angle is not meeting the needs of your business, the problems can quickly be recognized and cured rapidly before excessive time and exertion are spent on something that does not work.

If you utilize a light-footed approach, your group should team up for the ongoing run to guarantee the deliverables are met. A general task lead will help you meet the expectations. Little wins will assist your BI group, and business with staffing fabricates certainty. You will have the option to get results rapidly and pick up speed in your general BI system.

Recognize the Problem areas of Business Staff

You need to have the business staff needed to understand what information they need to make informed choices inside their areas of expertise. They might be physically arranging information to succeed or planning to write about their own. This information needs to be brought to the attention of the business insight group. Without their input, no enhancements can be made. The BI group should be able to get some insights that they can discuss with pioneers inside every office and figure out what questions exist today and how BI arrangements can help. If the “workarounds” that staff utilize are required for their positions or part of organization objectives, they ought to be incorporated into BI methodology. It might likewise be that a portion of these oddball strategies can be supplanted with more hearty examination and detailing.

Track down the Right Innovation

Innovation changes constantly, and your business intelligence procedure ought to include a strategy for using the latest technology. Something that currently works well may be to make changes within a couple of years and be supplanted by a far superior arrangement. The devices utilized may incorporate any or the entirety of the accompanying:

Information warehousingInformation investigationDashboardsSpecially appointed detailingInformation disclosure or information mining

The innovation should be coordinated, so your information does not wind up in storehouses. Consider adaptability and what these items mean for your business staff. A BI specialist has the experience to work with you to investigate which mix of instruments and administrations will place your business intelligence examiner in a good position.

Get the Most Benefits of Your Business Intelligence Strategy

Businesses that have embraced business intelligence will continue to grow and evolve. The above are the top helpful tips a business intelligence consultant would recommend.

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New 20+ pipeline operators for BQML

New 20+ pipeline operators for BQML

Today we are excited to announce the release of over twenty new BigQuery and BigQuery ML (BQML) operators for Vertex AI Pipelines, that help make it easier to operationalize BigQuery and BQML jobs in a Vertex AI Pipeline. The first five BigQuery and BQML pipeline components were released earlier this year. These twenty-one new, first-party, Google Cloud-supported components help data scientists, data engineers, and other users incorporate all of Google Cloud’s BQML capabilities including forecasting, explainable AI, and MLOps. 

The seamless integration between BQML and Vertex AI helps automate and monitor the entire model life cycle of BQML models from training to serving. Developers, especially ML engineers, no longer have to write bespoke code in order to include BQML workflows in their ML pipelines, they can now simply include these new BQML components in their pipelines natively, making it easier and faster to deploy end-to-end ML lifecycle pipelines.  

In addition, using these components as part of a Vertex AI Pipelines provides data and model governance. Any time a pipeline is executed, Vertex AI Pipelines tracks and manages any artifacts produced automatically.  

For BigQuery, the following components are available:







Allows users to submit an arbitrary BQ query which will either be written to a temporary or permanent table. Launches a BigQuery query job and waits for it to finish.

For BigQuery ML (BQML), the following components are now available:

BigQuery ML






Allow users to submit a DDL statement to create a BigQuery ML model.


Allows users to evaluate a BigQuery ML model.


Allows users to make predictions using a BigQuery ML model.


Allows users to export a BigQuery ML model to a Google Cloud Storage bucket 

New Components



Launches a BigQuery ML.FORECAST job and lets you forecast an ARIMA_PLUS or ARIMA model. 


Launches a BigQuery ML.EXPLAIN_FORECAST job and let you forecast an ARIMA_PLUS or ARIMA model


Launches a BigQuery ML.ARIMA_EVALUATE job and waits for it to finish.



Launches a BigQuery detect anomaly model job and waits for it to finish.

Model Evaluation


Launches a BigQuery confusion matrix job and waits for it to finish.


Launches a BigQuery ML.CENTROIDS job and waits for it to finish


Launches a BigQuery ml training info fetching job and waits for it to finish.


Launches a BigQuery ml trial info job and waits for it to finish.


Launches a BigQuery roc curve job and waits for it to finish.

Explainable AI


Launches a BigQuery global explain fetching job and waits for it to finish.


Launches a BigQuery feature info job and waits for it to finish.


Launches a BigQuery feature importance fetch job and waits for it to finish.

Model Weights


Launches a BigQuery ml weights job and waits for it to finish.


Launches a BigQuery ml advanced weights job and waits for it to finish.


Launches a BigQuery ML.PRINCIPAL_COMPONENTS job and waits for it to finish.


Launches a BigQuery ML.principal_component_info job and waits for it to finish.


Launches a BigQuery ML.ARIMA_COEFFICIENTS job and lets you see the ARIMA coefficients. 

Model Inference


Launches a BigQuery ML reconstruction loss job and waits for it to finish.


Launches a BigQuery explain predict model job and waits for it to finish


Launches a BigQuery ML.Recommend job and waits for it to finish.



Launches a BigQuery drop model job and waits for it to finish.

Now that you have a broad overview of all pipeline operators for BQML available, let’s see how to use forecasting ones in the end-to-end example of building demand forecast predictions. You will find the code in the Vertex AI samples repo on Github. 

Example of a demand forecast predictions pipeline in BigQuery ML

In this section, we’ll show an end-to-end example of using BigQuery and BQML components in a Vertex AI Pipeline for demand forecasting. The pipeline is based on the solving for food waste with data analytics blog post. In this scenario, a fictitious grocer, FastFresh, specialized in selling fresh food distribution, wants to minimize food waste and optimize stock levels across all stores. Due to the frequency of inventory updates (by minute of every single item), they want to train a demand forecasting model on an hourly basis. With 24 training jobs per day, they want to automate model training using an ML pipeline using pipeline operators for BQML ARIMA_PLUS, the forecasting model type in BQML. 

Below you can see a high level picture of the pipeline flow

Figure 1 – The high level architecture of the pipeline flow

From top to bottom: 

Create the training dataset in BigQuery 

Train a BigQuery ML ARIMA_PLUS model

Evaluate ARIMA_PLUS time series and model metrics

Then, if the average mean absolute error (MAE), which measures the mean of the absolute value of the difference between the forecasted value and the actual value, is less than a certain threshold:

Generate time series forecasts based on a trained time series ARIMA_PLUS model

Generate separate time series components of both the training and the forecasting data to explain predictions

Let’s dive into the pipeline operators for BQML ARIMA_PLUS.

Training a demand forecasting model

Once you have the training data (as a table), you are ready to build a demand forecasting model with the ARIMA_PLUS algorithm. You can automate this BQML model creation operation within a Vertex AI Pipeline using the BigqueryCreateModelJobOp. As we discussed in the previous article, this component allows you to pass the BQML training query to submit a model training of an ARIMA_PLUS model on BigQuery. The component returns a google.BQMLModel which will be recorded in the Vertex ML Metadata so that you can keep track of the lineage of all artifacts. Below you find the model training operator where the set_display_name attribute allows you to name the component during the execution. And the after attribute allows you to control the sequential order of the pipeline step. 

code_block[StructValue([(u’code’, u’bq_arima_model_op = BigqueryCreateModelJobOp(rn query=f”””rn — create model tablern CREATE OR REPLACE MODEL `{project}.{bq_dataset}.{bq_model_table}`rn OPTIONS(rn MODEL_TYPE = \’ARIMA_PLUS\’,rn TIME_SERIES_TIMESTAMP_COL = \’hourly_timestamp\’,rn TIME_SERIES_DATA_COL = \’total_sold\’,rn TIME_SERIES_ID_COL = [\’product_name\’],rn MODEL_REGISTRY = \’vertex_ai\’,rn VERTEX_AI_MODEL_ID = \’order_demand_forecasting\’,rn VERTEX_AI_MODEL_VERSION_ALIASES = [\’staging\’]rn ) ASrn SELECTrn hourly_timestamp,rn product_name,rn total_soldrn FROM `{project}.{bq_dataset}.{bq_training_table}`rn WHERE split=’TRAIN’;rn “””,rn project=project,rn location=location,rn ).set_display_name(“train arima plus model”).after(create_training_dataset_op)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e42c0a09bd0>)])]

Evaluating time series and model metrics

Once you train the ARIMA_PLUS model, you would need to evaluate the model before generating predictions. In BigQuery ML, you have ML.ARIMA_EVALUATE and ML.EVALUATE functions. The ML.ARIMA_EVALUATE function generates both statistical metrics such as log_likelihood, AIC, and variance and other time series information about seasonality, holiday effects, spikes-and-dips outliers, etc. for all the ARIMA models trained with the automatic hyperparameter tuning enabled by default (auto.ARIMA). The ML.EVALUATE retrieves forecasting accuracy metrics such as the mean absolute error (MAE) and the mean squared error (MSE). To integrate those evaluation functions in a Vertex AI pipeline you can now use the corresponding BigqueryMLArimaEvaluateJobOp and BigqueryEvaluateModelJobOp operators. In both cases they take google.BQMLModel as input and return Evaluation Metrics Artifact as output. 

For the BigqueryMLArimaEvaluateJobOp, here is an example of it used in a pipeline component:

code_block[StructValue([(u’code’, u’bq_arima_evaluate_time_series_op = BigqueryMLArimaEvaluateJobOp(rn project=project,rn location=location,rn model=bq_arima_model_op.outputs[‘model’],rn show_all_candidate_models=’false’,rnjob_configuration_query=bq_evaluate_time_series_configuration).set_display_name(“evaluate arima plus time series”).after(bq_arima_model_op)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e42c3528110>)])]

Below is the view of statistical metrics (the first five columns) resulting from BigqueryMLArimaEvaluateJobOp operators in a BigQuery table.

Figure 2 – A view of metrics resulting from BigqueryMLArimaEvaluateJobOp in BigQuery

For the BigqueryEvaluateModelJobOp, below you have the corresponding pipeline component:

code_block[StructValue([(u’code’, u’bq_arima_evaluate_model_op = BigqueryEvaluateModelJobOp(rn project=project,rn location=location,rn model=bq_arima_model_op.outputs[‘model’],rn query_statement=f”SELECT * FROM `<my_project_id>.<my_dataset_id>.<train_table_id>` WHERE split=’TEST'”,rn job_configuration_query=bq_evaluate_model_configuration).set_display_name(“evaluate arima_plus model”).after(bq_arima_model_op)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e42c0a1b050>)])]

Where you have a query statement to select the test sample to generate evaluation forecast metrics.

As Evaluation Metrics Artifacts in the Vertex ML metadata, you can consume those metrics afterwards for visualizations in the Vertex AI Pipelines UI using Kubeflow SDK visualization APIs. Indeed, Vertex AI allows you to render that HTML in an output page which is easily accessible from the Google Cloud console. Below is an example of a custom forecasting HTML report you can create.

Figure 3 – A custom forecasting accuracy report resulting from BigqueryEvaluateModelJobOp

Also you can use those values to implement conditional if-else logic using Kubeflow SDK condition in the pipeline graph. In this scenario, a model performance condition has been implemented using the average mean squared error in a way that if the trained model average mean squared error is below a certain threshold then the model can be consumed to generate forecast predictions. 

Generate and explain demand forecasts

To generate forecasts in the next n hours, you can use the BigqueryForecastModelJobOp which launches a BigQuery forecast model job. The component consumes the google.BQMLModel as Input Artifact and allows you to set the number of time points to forecast (horizon) and the percentage of the future values that fall in the prediction interval (confidence_level). In the example below it has been decided to generate hourly forecasts with a confidence interval of 90%. 

code_block[StructValue([(u’code’, u’bq_arima_forecast_op = BigqueryForecastModelJobOp(rn project=project,rn location=location,rn model=bq_arima_model_op.outputs[‘model’],rn horizon=1,rn confidence_level=0.9,rn job_configuration_query=bq_forecast_configuration).set_display_name(“generate hourly forecasts”).after(get_evaluation_model_metrics_op’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e42c16e3290>)])]

Then forecasts are materialized in a predefined destination table using the job_configuration_query parameter which will be tracked as google.BQTable in the Vertex ML Metadata. Below is an example of the forecast table you would get (only the five columns). 

Figure 4 – A view of the resulting ARIMA_PLUS forecasts

After you generate your forecasts, you can also explain them using the BigqueryExplainForecastModelJobOp which extends the capabilities of BigqueryForecastModelJobOp operator and allows to use the ML.EXPLAIN_FORECAST function which provides extra model explainability like trend, detected seasonality, and holiday effects.

code_block[StructValue([(u’code’, u’bq_arima_explain_forecast_op = BigqueryExplainForecastModelJobOp(rn project=project,rn location=location,rn model=bq_arima_model_op.outputs[‘model’],rn horizon=1,rn confidence_level=0.9,rnjob_configuration_query=bq_explain_forecast_configuration).set_display_name(“explain hourly forecasts”).after(bq_arima_forecast_op)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e42a9f25d50>)])]

At the end, here you will see the visualization of the overall pipeline you define in the Vertex AI Pipelines UI.

Figure 5  – The visualization of the pipeline in the Vertex AI Pipelines UI.

And if you want to analyze, debug, and audit ML pipeline artifacts and their lineage, you can access the following representation in the Vertex ML Metadata by clicking on one of the yellow artifact objects rendered by the Google Cloud console.

Figure 6  – The ML lineage of demand forecast pipeline in Vertex ML Metadata.


In this blogpost, we described the new BigQuery and BigQuery ML (BQML) components now available for Vertex AI Pipelines, enabling data scientists and ML engineers to orchestrate and automate any BigQuery and BigQuery ML functions. We also showed an end-to-end example of using the components for demand forecasting involving BigQuery ML and Vertex AI Pipelines. 

What’s next

Are you ready for running your BQML pipeline with Vertex AI Pipelines? Check out the following resources and give it a try: 


BigQuery ML

Vertex AI Pipelines

BigQuery and BigQuery components for Vertex AI Pipelines

Code Labs

Intro to Vertex Pipelines

Using Vertex ML Metadata with Pipelines

Vertex AI Samples: Github repository

Video Series: AI Simplified: Vertex AI

Quick Lab: Build and Deploy Machine Learning Solutions on Vertex AI


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Aiding Architecture & Engineering Firms with Data-Driven Learning

Aiding Architecture & Engineering Firms with Data-Driven Learning

Data analytics is incredibly valuable for helping people. learn. More institutions are recognizing this, so the market for data analytics in education is projected to be worth over $57 billion by 2030.

We have previously talked about the many ways that big data is disrupting education. Big data isn’t just helping with education in the field of academia. Individual companies are also finding ways to take advantage of data to foster learning.

One of the fields that is leveraging data analytics to educate employees is the engineering and architecture sector.

Using Big Data to Improve Learning in the Architecture and Engineering Field

Architecture and Engineering Firms often rely on experts with specialized skills for delivering digital design outputs. Additionally, they may be required to hire external contractors and corporate trainers to accomplish the project. These experts need a detailed understanding of big data, since it is crucial for handling these tasks.

This dependency poses the risks of increased costs, time and effort, and project delays. They can use data analytics to make sensible decisions when carrying out these projects.

To tackle the budget and resources limitation and stay up-to-speed with the market trends during the pandemic, Architecture and Engineering firms are progressing toward digital learning platforms for sector-specific technical and non-technical training for all employees. They can use data analytics and predictive analytics tools to anticipate these trends more easily.

E-learning and m-learning (mobile learning) courses are available to Architects and Engineers to enhance their professional knowledge, thereby transforming the landscape of continuous professional development (CPD) within the work environment.

Using Data Analytics to Promote Learning in The Construction Sector

As the construction sector is undergoing a rapid digital transformation, architecture and engineering firms are adapting to online learning management systems for the continued professional development of their employees. The digital learning platforms for architecture and engineering firms are customized to perform multiple functions such as assigning and reporting completion of courses, tracking license requirements, and identifying employee skill gaps. They can use data analytics tools to monitor progress and help people learn more easily.

The training content is specially designed to meet the needs of architects and engineers in the digitally progressing market. The more data analytics algorithms are utilized, the easier it will be to deal with these changes.

Micro-learning Methodology

Supporting the growing trend of bite-sized micro-learning or nano-learning, architects and engineers can access condensed information at any time and place based on their convenience. The micro-learning courses enable the professionals to grasp most information in an engaging format. These courses use data analytics tools to assess skill retention.

Professionals in the construction sector are required to multi-task both on-site and off-site. Using the micro-learning methodology, they can easily access all training materials whenever they need them from any device.

It ensures quick resolutions and coordination between the multiple stakeholders involved in the delivery of a construction project.

How Does Data-Driven Learning Empower Architecture And Engineering Firms?

Architecture and Engineering firms plan, design, and enable the construction of complex infrastructure and buildings. A comprehensive cloud-based digital learning platform with sophisticated data analytics tools enables employees to access multi-modal training material.

The successful delivery of an infrastructure and building project depends largely on effective collaboration between architects, landscape architects, structural engineers, mechanical, electrical, and plumbing (MEP) engineers, designers, and contractors.

The learning management system for architecture and engineering firms integrates learning modules and specific design and management tools that rely on complex data analytics tools. It enables the learner to understand building information modeling (BIM) software.

BIM, relatively new technology in the construction sector, is used to manage information for a project throughout its lifecycle – from planning to construction and operations.

Types of Online Training Courses

Professionals in the construction sector need to stay up to speed with the advancement of tools and technology.

The online training courses for the continued professional development of employees in an architecture and engineering firm may be categorized broadly into two groups:

Technical TrainingNon-Technical Training

Technical Training

Technical training helps employees become better professionals by improving the skills needed to perform their roles. Whether they are designers, architects, engineers, or IT professionals, each expert has a scope for improvement in their respective knowledge sector.

Technical Training courses focus on the development of hard skills as described below.

1. Software Training

This includes training for design and BIM software such as Revit, Sketchup, Photoshop, Illustrator, STAAD, 3ds Max, 3D Printing, and AutoCAD.

The construction industry has also started exploring impactful technological innovations such as Virtual Reality, Augmented Reality, and Artificial Intelligence to train professionals.

2. Computer Apps Training

This may include training for improving presentation skills, report writing skills, advanced training for Microsoft Apps such as Powerpoint, Word, and Excel, and also custom apps installed by the specific Architecture and Engineering firm for management of documents and deliverables, coordination between teams, etc.

3. Product Knowledge Training

To sell a product successfully to a client, the professional needs to be fully aware of the product’s features, benefits, costs, and uses. The firm usually conducts product knowledge training for its employees when it introduces a new feature or product in the market.

Non-Technical Training

Non-Technical training focuses on the soft skills development of the professionals. It helps in shaping their behavior and interpersonal relationships.

It also enables the employees to interact harmoniously with their colleagues and clients.

The soft skills involve effective communication, problem-solving, decision-making, client management, and company ethics.

Further, non-technical training includes Onboarding and Orientation Training for new hires and Compliance Training.

Benefits Of Digital Learning In Architecture And Engineering Firms

Data-driven learning in the construction industry is proving to be highly beneficial for professionals. Some of the advantages of using data-driven learning are listed below.

Data-driven learning has enabled professionals to learn about the latest trends and technology in construction and carve out their career paths based on their interests and comfort levels.Architects and engineers are evolving and doing more than designing and supervising the construction of buildings and equipment. With unlimited access to data, information, and analytics, they can broaden their ambits in design and experiment with modeling, robotics, 3D printing, and virtual and augmented reality.The availability of training modules on all devices enables architecture and engineering professionals to streamline their processes, provide quick iterations, and deliver solutions.Custom training courses provide the professionals with the opportunity to collaborate and enhance their performance collectively when working on data-driven education projects.Interactive digital learning courses simulate an engaging environment for professionals and foster a sense of ownership over their professional growth.The availability of training courses at any time gives the learners the flexibility to learn as and when they can spare time from their busy work schedules.

Data-Driven Learning is the Future of the Engineering Sector

Big data is the future of education. Data-driven learning has become an essential part of the continued professional development of employees of all sectors of the business world, including the construction sector.

Architects and engineers have a plethora of online training courses in which they can learn to upskill themselves and enhance their professional performance.

Moreover, data-driven learning platforms provide flexibility to learners. They can access the training material at a convenient time and from any location. This keeps them motivated and engaged in learning and improving their technical and non-technical skills.

Architecture and Engineering firms can, thus, build efficient teams and deliver projects in a more effective and timely manner.

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AI-Driven SEO is Becoming Essential for Modern Marketing

AI-Driven SEO is Becoming Essential for Modern Marketing

Artificial intelligence is one of the most disruptive forms of technology shaping the marketing profession since the dawn of the Internet. Here are some statistics on the importance of AI in marketing:

48% of marketers feel AI makes a greater difference than anything else in affecting their relationship with customers51% of e-commerce companies use AI to improve the customer experience64% of B2B marketers use AI to guide their strategy

One of the most important ways companies are taking advantage of AI is with SEO. They can use AI to improve both their onsite and offsite SEO strategies to reach more customers through organic search.

AI Helps Companies Reach More Online Customers through SEO

In the modern-day world, having a digital presence is becoming increasingly important. Internet usage is on the rise and embracing digital marketing tools can boost brand awareness and business success.

Traditional marketing, such as brochures, leaflets, and billboard ads, are still effective. However, they are now shadowed by the impacts of digital marketing, which relies very heavily on data analytics and AI technology.

There is a variety of digital marketing strategies that you can use. One of the most well-loved strategies is search engine optimization (often shortened to SEO). You can use AI technology to guide your SEO strategy and streamline many important aspects of it.

If you’re not sure what SEO is, you’ve come to the right place. In this article, we’re going to cover what SEO is and why it’s vital for your business. After providing an overview of the importance of SEO, we will get into the nuts and bolts of using AI to perfect it.

If you decide that you need some extra help after reading this post, get in touch with Edmonton SEO specialists for expert guidance.

What is SEO?

Search engine optimization is a highly effective way to increase your online brand visibility. It involves using niche-specific keywords and key phrases throughout your content, whether it’s your website copy or your social media copy.

When an individual is searching for something on the internet, the search engine uses artificial intelligence to find the most relevant websites. If your website contains lots of commonly searched terms within your industry, it will appear near the top of the results pages when a user makes a relevant search.

Why is SEO Important For Your Business?

When you successfully use SEO, you can increase your website’s rankings on search engine results pages (SERPs), such as Google. High visibility, increases the chances of more internet users coming across your site as they are browsing the internet.

The more users that are viewing your content, the more leads you can generate and the more sales you can make. Plus, high visibility websites are seen as more reliable by Google, which can increase your authority as a brand.

It’s pretty much impossible to create an effective digital marketing strategy without using SEO. It’s an essential component in the online success of your brand.

As a business owner, you will know how important website traffic is to the success of your company. Website views is a metric that you should always track because it’s a key indicator of your brand awareness and growth over time.

Using SEO is a completely free way to increase your website traffic significantly and get more eyes on your brand. Once potential customers have landed on your website, they may browse your products or services and make an inquiry with your team.

If you’re working with a limited budget (or even if you’re not), you can use SEO to boost the success of your business without dipping into your budget. SEO is one of the few forms of marketing that enables you to generate more money without making a significant investment.

Using AI to Improve Your SEO Strategy

AI technology can be very helpful for modern SEO. AI is especially important for local SEO. Here are some ways to incorporate AI in your SEO strategy:

Neil Patel points out that more marketers are using AI to identify more linkbuilding opportunities for SEO. Google depends on link analysis algorithms to rank sites higher in its SERPs. You can use AI tools like Ahrefs and SEMRush to find new places to get links. You can also use AI to identify new keyword opportunities with tools like BrightEdge. AI algorithms don’t just spit out new keywords that you can target. They help assess trends in search volume and competitiveness, which are equally important as the quantity of monthly searches. You can use AI to generate new content. Some companies find Creative Commons videos on YouTube and use machine learning algorithms to convert the sound into text to publish on their sites.

AI technology is incredibly valuable for modern SEO. You just have to find the right tools and use them strategically.

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Streamline data management and governance with the unification of Data Catalog and Dataplex

Streamline data management and governance with the unification of Data Catalog and Dataplex

Today, we are excited to announce that Google Cloud Data Catalog will be unified with Dataplex into a single user interface. With this unification, customers have a single experience to search and discover their data, enrich it with relevant business context, organize it by logical data domains, and centrally govern and monitor their distributed data with built-in data intelligence and automation capabilities. Customers now have access to an integrated metadata platform that connects technical and operational metadata with business metadata, and then uses this augmented and active metadata to drive intelligent data management and governance. 

The enterprise data landscape is becoming increasingly diverse and distributed with data across multiple storage systems, each having its own way of handling metadata, security, and governance. This creates a tremendous amount of operational complexity, and thus, generates strong market demand for a metadata platform that can power consistent operations across distributed data.

Dataplex provides a data fabric to automate data management, governance, discovery, and exploration across distributed data at scale. With Dataplex, enterprises can easily organize their data into data domains, delegate ownership, usage, and sharing of data to data owners who have the right business context, while still maintaining a single pane of glass to consistently monitor and govern data across various data domains in their organization. 

Prior to this unification, data owners, stewards and governors had to use two different interfaces – Dataplex to organize, manage, and govern their data, and Data Catalog to discover, understand, and enrich their data. Now with this unification, we are creating a single coherent user experience where customers can now automatically discover and catalog all the data they own, understand data lineage, check for data quality, augment that metadata with relevant business context, organize data into business domains, and then use that combined metadata to power data management. Together we provide an integrated experience that serves the full spectrum of data governance needs in an organization, enabling data management at scale.

“With Data Catalog now being part of Dataplex, we get a unified, simplified, and streamlined experience to effectively discover and govern our data, which enables team productivity and analytics agility for our organization. We can now use a single experience to search and discover data with relevant business context, organize and govern this data based on business domains, and enable access to trusted data for analytics and data science – all within the same platform.” saidElton Martins, Senior Director of Data Engineering at Loblaw Companies Limited.

Getting started

Existing Data Catalog and Dataplex customers and new customers can now start using Dataplex for metadata discovery, management and governance. Please note that while the user experience interface is unified via this release, all existing APIs and feature functionalities of both products will continue to work as before. To learn more, please refer to technical documentations or contact the Google Cloud sales team.

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The next generation of Dataflow: Dataflow Prime, Dataflow Go, and Dataflow ML

The next generation of Dataflow: Dataflow Prime, Dataflow Go, and Dataflow ML

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing artificial intelligence according to IDC, yet the growing complexity of data types, heterogeneous data stacks and programming languages make this a challenge for all data engineers. With the current economic climate, doing more with cheaper costs and higher efficiency have also become a key consideration for many organizations.

Today, we are pleased to announce three major releases that bring the power of Google Cloud’s Dataflow to more developers for expanded use cases and higher data processing workloads, while keeping the costs low, as part of our goal to democratize the power of big data, real time streaming, and ML/AI for all developers, everywhere.

The three big Dataflow releases we’re thrilled to announce in general availability are:

Dataflow Prime – Dataflow Prime takes the serverless, no-operation benefits of Dataflow to a totally new level.  Dataflow Prime allows users to take advantage of both horizontal autoscaling (more machines) and vertical autoscaling (larger machines with more memory) automatically for your streaming data processing workloads, with batch coming in the near future.  With Dataflow Prime, pipelines are more efficient, enabling you to apply the insights in real time.  

Dataflow Go  – Dataflow Go provides native support for Go, a rapidly growing programming language thanks to its flexibility, ease of use and differentiated concepts, for both batch and streaming data processing workloads. With Apache Beam’s unique multi-language model, Dataflow Go pipelines can leverage the well adopted, best-in-class performance provided by the wide range of Java I/O connectors with ML transforms and I/O connectors from Python coming soon.  

Dataflow ML – Speaking of ML transforms, Dataflow now has added out of the box support for running PyTorch and scikit-learn models directly within the pipeline. The new RunInference transform enables simplicity by allowing models to be used in production pipelines with very little code. These features are in addition to Dataflow’s existing ML capabilities such as GPU support and the pre and post processing system for ML training, either directly or via frameworks such as Tensorflow Extended (TFX).

We’re so excited to make Dataflow even better.  With the world’s only truly unified batch and streaming data processing model provided by Apache Beam, the wide support for ML frameworks, and the unique cross-language capabilities of the Beam model, Dataflow is becoming ever easier, faster, and more accessible for all data processing needs.

Getting started

To get started with Dataflow Go easily, see the Quickstart and download the Go SDK.To learn more about Dataflow Prime, see the documentation. To learn more about Dataflow ML and RunInference, read about the new RunInference Beam transform on the Apache Beam website.

Interested in running a proof of concept using your own data? Talk to your Google Cloud sales contact for hands-on workshop opportunities or sign up here.

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Proven VPN Protocols to Safeguard Your Data

Proven VPN Protocols to Safeguard Your Data

Are you concerned about your data privacy? You are going to want to invest in a VPN. Few other safeguards will be equally effective.

Keep reading to learn why this is so important.

A VPN is Going to Be Necessary for Data Privacy – Even if Data Protection Laws Are Passed

Data privacy is becoming a greater concern than ever. One poll from 2021 showed that 83% of Americans supported legislation to protect their data privacy.

While the growing support for data privacy legislation might be good news, it is important not to get too optimistic yet. Despite the growing concerns about data privacy, little effort is being made on that front. Even if legislation passes, improvements in data privacy might not be as encouraging as you would like to think.

The truth is that the implications of new privacy regulations might not be as strong as we would like. Many companies might not adhere to them either and the legislation might not lead to any real improvements if they are not enforced. Also, many websites are still based in other countries that might not follow the new legislation.

If you want to protect your online privacy, you are going to need to use a VPN. VPNs offer tremendous protection for anyone wanting to protect their anonymity and data privacy.

VPNs are Vital for Modern Data Protection

Virtual Private Networks (VPNs) are a priceless remedy for a problem that is frequently encountered today: maintaining your data security and anonymity online. The large numbers of interested VPN users are most likely keen on both getting a VPN and finding out about its intricate details. This will help them improve their data privacy considerably.

This article is written for anyone who is keen on getting more familiar with VPN protocols for optimal data privacy and knowing which ones you should look for.

What is VPN Protocol?

Following the right protocols for VPNs is crucial. They make your internet communications quick and safe by securing and encrypting your online data. Depending on the VPN protocol you are currently using, your VPN may be faster or more secure. These act as portals via which your web traffic can travel, which keeps your data safe and secure. There are secure tunnels. Others provide incredible streaming rates for people exchanging large volumes of data.

There are numerous varieties of protocols, each with a unique set of advantages. Mobile device-friendly protocols exist. On your WiFi router, you can also install additional programs. Several provide greater internet protocol security. To have the best browsing experience, using a VPN protocol is crucial.

There are many free VPN download services that use highly secure protocols.

Types of VPN Protocols for Improving Your Data Privacy

If you are trying to protect your data privacy with a VPN, then you will want to follow these protocols.

1.      Point-to-Point Tunneling Protocol (PPTP)

The Point-to-Point Tunneling Protocol (PPTP) is one of the first VPN protocols to have ever been developed. Microsoft created PPTP in the middle of the 1990s, which was included in Windows 95 with a dial-up connection in mind. But as technology developed, PPTP’s fundamental data encryption was soon broken, putting its underlying security at risk.

For customers who may not require strong data encryption, it can offer the fastest connection speeds because it lacks many security measures offered in other current protocols. However, even if PPTP is still used in some applications, most service providers have now changed to quicker, more dependable protocols for securing their data.

2.      L2TP/IPSec

The PPTP VPN protocol has been replaced by Layer 2 Tunnel Protocol. This protocol, which lacks out-of-the-box data privacy or encryption, is typically used in conjunction with the security protocol IPsec. L2TP/IPsec is extremely secure once put into use and has no known flaws.

3.      OpenVPN

The majority of the leading VPN service providers employ this open-source protocol. It was created with a wide purpose in mind and debuted in 2001. It is simple for third-party VPN clients to use because it wasn’t included in PCs or mobile devices. Many platforms can support OpenVPN, but you’ll need to include third-party software to make it work. Due to its simplicity of configuration, this protocol may support a wide range of ports and encryption options.

4.      SSTP

Microsoft created and included this data protection protocol in Windows Vista. For your online business, you can also rely on SSTP, particularly if you use Microsoft machines. You can use SSTP on Linux, Android, and macOS even though it is not an open-source protocol like OpenVPN. However, connecting it from macOS could be difficult.

Firewalls without a complicated configuration can be bypassed using SSTP. Despite not being the quickest business, it operates just as quickly as OpenVPN. Because it protects your data with SSL 3.0 encryption, you can depend on this protocol for your online business.

5.      IKEv2

Cisco and Microsoft created the protocol in question. It is one of the fastest protocols you can rely on as a business owner and is mobile-friendly. This protocol is for you if your business activities frequently involve downloading and transferring huge files. IKEv2 facilitates switching between WiFi and mobile Internet, which guarantees a consistent connection to a VPN tunnel. This protocol rapidly reconnects you if, for example, service abruptly stops while your traffic is traveling through the tunnel.

6.      Wireguard

A relatively recent open-source VPN protocol still in development is called Wireguard. It is Linux-based and reportedly performs better than both OpenVPN and IPSec. It is already accessible on a number of platforms even though it is still being developed. It’s still uncommon to find in a consumer VPN app because it’s so new.

Which is the Best VPN Protocol to Protect Your Data?

The best VPN protocol currently used is OpenVPN. It is ideal to utilize when data privacy and security are important, and you don’t mind slower speeds or less flexibility. In places with strict internet censorship or when torrenting, for instance, you should utilize OpenVPN to access free Internet. It is also great for data protection.

For mobile VPN users, IKEv2 is an additional useful protocol. It excels at managing frequent and abrupt network changes because of its MOBIKE protocol. It still performs far better than other VPN protocols like OpenVPN.

Use the Right VPN Protocols for Data Privacy

Pay close attention to the VPN protocols listed in a provider’s list of features when selecting a VPN service to safeguard your online data. While selecting a service that offers a low-security protocol, like PPTP, may be appropriate for your needs, there’s a good possibility that you’ll need something with greater security.

In fact, there is no right or incorrect protocol choice; instead, focus on choosing a supplier whose protocols satisfy all of your requirements.

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AI Technology Offers Time Management Benefits in the Workplace

AI Technology Offers Time Management Benefits in the Workplace

AI technology has become incredibly helpful for companies trying to boost productivity. There are a lot of invaluable applications that use AI to bolster efficiency, lower costs and help companies improve the quality of their products and services. According to research from Oberlo, 91% of businesses have invested in AI technology to achieve these benefits.

One of the biggest benefits of AI in the workplace is that it can help improve time management. Employees using these tools can help improve organization efficiency considerably.

Using AI Technology to Improve Time Management within Your Company

Everyone needs to focus on their work when they are in the office. Whether you’re the CEO or an ordinary employee working on daily tasks, you need to be perfectly focused and productive when doing your job.

Fortunately, growing advances in AI technology are making it easier to stay focused and work efficiently. You need to take advantage of these tools to the best of your ability to get the desired results. Keep in mind that your competitors are also investing in AI technology, so you will want to stay up on these developments to stay competitive.

Sometimes staying focused is easier said than done. There are so many distractions, and many employees are unaware of how much time they spend on useless stuff. Instead of doing this, they need to be aware of how they are working and what they can do to improve themselves. AI technology can’t completely eliminate all of the distractions, but it can help make them more manageable.

In this article, we’re talking more about using AI to manage your work time better and stay more productive, motivated, and successful. Read on to find out the three main things you need to do to achieve the goals we just mentioned.

1. Use AI to Track and Optimize Team Performance

Tim Stone of IBM has mentioned that AI technology is the future of time management within organizations. He points out that a growing number of companies are using AI to assess how much time teams and individual employees spend on any given task. They can use this data to cut out certain tasks that waste a lot of time.

Stone also points out that AI technology has made it easier for team leaders to ensure their employees attend events and in-person meetings they are required to participate in. AI has helped with location-based time tracking, which is essential for these tasks. AI algorithms are also embedded in many apps employees use, so you can make sure they are making use of apps that help them get their jobs done effectively.

2. Automate Important But Rudimentary Tasks

AI technology is also essential for automating certain functions. There are many different types of tasks that can be automated. For example, certain marketing processes can be automated with Salesforce technology, which relies heavily on AI. Adobe Photoshop and Illustrator have also evolved over the years due to AI, which helps users automate many repetitive tasks through the use of actions and scripts.

3. Figure Out When to Take Short Breaks

Short breaks while working can significantly improve your impact on the workload. It may sound strange but taking more time off work can give you better results than trying to work non-stop without rest.

Suppose you’ve been working for 30 minutes. Taking a break of 5 minutes will reprogram your brain and help it relax for a moment, only to take it back into action. On the other hand, trying to work non-stop will create pressure to achieve more, and your brain will resist the need to work.

What you need to do is take short breaks in which you will not do anything mentally. You should take a walk, go in the fresh air, look into the distance, laugh with colleagues, or something similar. Avoid mental work and cut the screen time because that makes your brain tired.

Unfortunately, figuring out when to take breaks is not always easy. You can use calendar tools to see when you took your breaks on different days. You can sync them with AI-based productivity management tools to see how productive you were on days when you took breaks at different times. This can help you time your breaks for maximum results.

4. Remove Distractions

A number of new tools use AI to help people minimize distractions. The StayFocsd app limits the time that you spend on different websites, which keeps you from digital social loafing. You can monitor employee website use and use similar apps to restrict them from spending time on sites or apps that are going to waste their time.

5. Prioritize Better

Prioritizing tasks is highly valuable for getting everything done fast and accurately. Many people are trying to handle everything simultaneously, and they’ll be working on many projects simultaneously. Still, if you look at the time spent on everything, you realize that you would’ve done it faster if you prioritized the most important ones first.

Always put the most important projects first, and only when these are finished go for the next ones. It is scientifically proven that multitasking confuses the brain, and even if you have placed multitasking as a skill in your CV, try not to do it if you want better work results.

What you need to do is see what’s on the list at the beginning of the day. Most people have coffee when they arrive at the office, so use this time to see what’s on the “menu” before starting. Focus on those tasks that have the highest priority, and take them off the list as you move forward.

You can use AI tools to better identify the tasks that lead to the best results. This will help you prioritize them better.

6. Install time-tracking software to keep you focused

A time-tracking app can significantly improve your work and show you where you’re losing time. When it comes to important projects, you can’t afford to lose time on useless things, and every minute counts, so installing one of these is highly valuable.

A great project management time-tracking software will always notify you about what’s coming next, what you need to do to get the project tasks on time, and whether you may be missing an appointment or a meeting with clients.

These apps are excellent for everyone who’s struggling to get things done. If set right, the software can also make the prioritization for you. You will never worry again about what’s more important; with it, you’ll never lose track of time.

AI Technology is Valuable for Improving Time Management

These points are essential for everyone struggling with getting things done in time. Sometimes all we need is a reminder about the things we forget about, while other times, we need something to push us in the right direction.

That’s why time-tracking software can be so valuable for your productivity at work, and the other tips we mentioned can only add to your success. Focus on one thing at a time, know what to work on, and be sure that you’re doing great.

If you think you’re the one responsible for always being late, reconsider your old job and the workload there, the difficulty of the tasks, and the time frame in which you’ve been working. You may be working twice as hard, but this job requires more, which is why you want to improve yourself.

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