Data Extraction Done Right

Data, data and more data. Day in and day out gigabytes of data are churned in. Data is wealth for any business. But are they able to extract the most out of the data? That’s a zillion-dollar question. Zillion? Are you reading it right?

Yes, of course. As the blog is written in 2022, the data gathered across the world ss 100 ZB. The projection is to reach 200 ZB by 2025. It is about the right time to rename Big Data as Fat data or Jumbo data or Zilly Data!

 The myth that Big data is used only by large enterprises is already busted and now medium and small-scale enterprises are using Big data. Again big data is a relative term and its size varies from business to business. Every organization wants to harness the data power it wields to become the top player in its respective field. But are they getting the most from the data that is available at their disposal? Are they able to understand the customer trends?

Industry experts are opinionated on which process is more important, extraction of data, analyzing the data or identifying the pattern or predicting the future trend. But it is not the size of the data or the methodology that matters, rather how it is effectively used to achieve the intended organization’s business goals and objectives defines its success. 

When analyzing 100 ZB of data is near impossible, the cost that needs to invest in infrastructure would be skyrocketing. Does analyzing that amount of data worth it at all? One cannot have a straight yes or no to that question. One may or may not need that amount of data. Amazon may need it. Google might be analyzing it.  For others, a subset of that data should be enough for succeeding in their line of business. The key to success lies in building the right data framework to leverage the right data for your organization’s success.

How to go about it? How can one identify the right data framework that will get the most out of the data? Industry experts have a multitude of processes, tools and techniques for that.

The most plausible approach has to be top-down starting with business strategy or vision.

When the business embarks on the big data journey, the first step should be to define the organization’s business strategy and vision.  The next step is to translate into achievable objectives and goals. Aligning to it, build your data framework. The data framework should be able to cater to all of the business case planning. With proper business case planning, one should be able to measure the return on investment. If the set data framework is right, the return on investment should be equal to or greater than the expected one and it should not be a one-off success story, but rather a sustainable one that keeps the business spiraling in an upward trend.

To achieve the right data framework, the main components can be listed as 4 A’s

1. Adoption

2. Accuracy

3. Analytics &

4. Artificial Intelligence

Adoption

Adopting the right data management process is halfway to winning the race. Analyze the organization’s existing data management process. Identify and decide on the way forward either to build it from scratch or re-platform. Is the data that is in-house sufficient or should we go for external or synthetic data? Should the infrastructure be on-premises or cloud? With the growing data size, it is advisable to go for the cloud for security, scalability, portability and future innovation requirements. 

Select the right infrastructure tools and technology for data privacy, security and protection. Another critical step is to make your stakeholders adopt the data management process. Educate the employees through a proper communication plan on the need for data management and the returns coming out of it.  Make your employees more data literate by generating dashboards that will enhance the work experience, decision-making capabilities and faster results.

Accuracy

For businesses that rely on data-driven decision-making, data accuracy is critical. Companies that lack accurate data may make suboptimal decisions that cost them time and money.

The most important aspect of data quality is data accuracy. It ensures that your company’s business processes are founded on reliable and appropriate information, resulting in improved decision-making capabilities across the board, including planning, forecasting, budgeting intelligence, and more!

Data accuracy is crucial because erroneous predictions are made as a result of inaccurate data. Time, money, and resources are squandered if the expected outcomes are incorrect.

Accurate data improves decision-making confidence, boosts efficiency, productivity, and marketing, and lowers costs.

1. Data Accuracy Enables Better Decision Making

2. Improved Productivity

3. Lower Operational Cost

4. Improved Marketing

5. Aids in Security and Regulatory Compliance

Analytics

Analytics is not only for insight but gives us foresight into the future. Be it data analytics or business analytics it helps to improve return on investment, business outcomes and operational efficiency apart from providing a superior customer experience. A scalable analytical tool should be in place to manage streams of data to provide the right analytics and trends. Proper analytics will help to achieve the following

  • Expansion to a new market
  • Launching a new service
  • Understanding market position
  • Understanding competitors
  • Understanding customers & personalizing customer services
  • Enabling efficient company processes

 Artificial Intelligence

Artificial Intelligence or AI is the best of both worlds. It works like machines and thinks like humans. AI is not replacing your workforce but making your workforce more efficient by taking the considerable workload off their shoulders through continuous automation.  To start with machine learning is the core of AI, which learns algorithms from data mining. The newly acquired intelligence improves its performance and analyzes more plausible data that is required for analytics. While machine language concentrates on data, NLP or natural language processing improves communication with humans. The critical success criteria for AI in any organization is the right endpoint integration of AI into the operational process for maximum returns. 

The above-listed 4 A’s are the key elements for a holistic approach to any organization’s data framework. Over the due course of time, it will change the current operational processes. It helps to understand the current business performance, and futuristic growth prospects and the trend projection will help you to understand the business relevance in the ever-changing ecosystem.

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