Turning Data into Actionable Insights

Data and Insights

Insights are the new gold not the data as data is worth very little unless this data is turned into critical actionable insights. These insights can be used to support decision making and can help in refining the design and manufacturing processes. Machine learning algorithms can be used to accomplish different applied data mining tasks. These tasks can be descriptive analytics, predictive analytics, diagnostic analytics, and prescriptive analytics. Descriptive data analytics provides insight into the past and the present while predictive analytics forecasts the future. Diagnostic analytics provides root-cause analysis and perspective analytics advises on possible outcomes and their anticipated impacts.

Data Analytics

Descriptive data analytics provides a better understanding of the data and its nature and identifies patterns or relationships in the data. These descriptive models answer the following questions:

• What has happened?
• What happens now?
• What is the trend of a certain variable?
• What is the relationship between variables?
• How an item is performing w.r.t other item or a benchmark item?

Predictive models make prediction about future values of data and forecasts new proprieties instead of just exploring data properties like in case of descriptive analytics. These models answer the following questions:

• What would happen?
• When would it happen?
• Where would it happen?

Diagnostic analytics provides root-cause analysis to answer questions like:

• Why did it happen?
• Why would it happen?

Last but not least, prescriptive models focus on decision support. This decision support or recommendation engine answers the following questions.

• How would the predictions obtained from predictive models impact everything else?
• What are the proactive decision/actions to be made?
• How we benefit from predictions/recommendations?
• What are the best actions to make?
• What is the best time to take this action?
• What would be the impact of this action?

#descriptive-analytics #deep learning

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Actionable Insights Steer Industries Towards Better Business Decisions

In the current digital world, there is no industry that won’t benefit from actionable insights. With time, every industry will adapt to it. For example, in financial technology, a problem that requires actionable insights is credit risk assessment. A well-structured fintech algorithm can easily use machine learning to suggest to employees whether they can approve and reject a loan application. That advice is actionable insight.

The algorithm assesses the applicant’s probability of load success by training on heaps of historical data and market conditions. The result of the algorithm is the insightful judgment of the applicant’s acceptable or unacceptable risk. Action, in this case, is the human loan agent’s decision to grant the loan or deny it. The majority of businesses in today’s day and age depend on such insights. They require actionable insights incorporated in the workflows to drive better business outcomes without people having to leave their primary tasks to sieve through data for answers.

#big data #data analytics #action #decision to #actionable insights #actionable insights steer industries towards better business decisions

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data