Building a Data Analytics Strategy which can drive the business growth

Every one of us is now aware of the importance of data for running successful businesses, getting the advantage of being ahead of the competition and optimizing the operations. Organizations are ready to spend on data strategy, which can lead them to the right business growth. This leads us to a question, What kind of strategy do they need to adopt as part of their data initiative? According to Gartner, there are three types of data strategies.

Among the above three categories, whatever your current state is, "Data Analytics as a Driver" should be the future state for every enterprise. In the subsequent sections, I will describe the steps required to build a data strategy that can help drive the business.

Experts might disagree on certain steps, but this is based on my personal experience and engagement with large enterprises during their data strategy development.

1.????Bringing the Stakeholders Onboard

In large enterprises, data silos may exist not by choice but due to the volume of business operations. Different internal and stakeholders own these silos. When you are working on a Data Management strategy, the first step is to get buy-in from all stakeholders and bring them onboard for an enterprise-wide data management strategy. This is not a simple job; it requires a lot of effort and significant push from top management as well. Why do I consider this a first step in the process? Because this will answer two critical questions:

• Who owns what?
• Who is accountable?

Answers to these questions will help your organization drive the data management strategy.?

2.????Discover the Data Assets

Gone are the days when data assets were known to every business stakeholder all the time. The way technology drives businesses, new data points are becoming more decisive, and organizations must keep digging new points impacting their business. Internal data points are essential to maintain; external data points are getting precedence over them. These points can be related to social trends, economic trends, competitor data and regulatory data etc. A recent example from one of our customers who started using state opening dates after COVID-19 restrictions to be added as part of one point for their sales and inventory forecasting.

This discovery of data assets is an ongoing process and needs continuous focus with changing dynamics of the world.

3.????Build the Data Supply Chain

As a data management leader, you need to manage data in terms of a supply chain where your source data from internal and external points, perform certain operations to make this as finished good for consumption and then supply that to the consumers. This means you need to build a data ecosystem which serves the supply chain requirements.?

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The data ecosystem should be agile enough to onboard new sources quickly or deliver data seamlessly, meeting all the SLAs for business. This is the most critical and expensive step in data strategy, as this will drive the future state of your strategy. If compromised, this can lead to failure.

Data ecosystem foundations should be strong enough to support all technology advancements and support structured and unstructured data assets. Cloud adoption should also be considered at this stage because it requires less investment and provides more agility than on-prem solutions.

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4.????Govern the Data

Once a successful data supply chain is established, you need to build the culture for data governance. Why I am underlining the word "Culture" is because governance is not a technology problem only. It requires the same investment in your people and processes. As part of data governance, you need to:

• Execute the data stewardship program
• Enrich your data through metadata
• Build data quality measurement processes
• Ensure regulatory compliances
• Ensure data security and privacy
• Establish data democratization

Again, this is not a one-time process, it requires iterations and continuous improvements based on the addition and discovery of new data assets.

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5.????Offer "Data-as-a-Service" to Consumers

In this agile world, the consumers do not wait for IT teams to deliver data according to requirements. Now organizations are heading towards offering data-as-a-service (DaaS), where consumers can register to a service to get required data in their choice of format and frequency. All the data collection, curation and enrichment, should happen as part of the data supply chain. Data consumers can be any analytical application, a machine learning model, a data analyst, a data scientist, a downstream application, or an external entity having access to certain data points.

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Key characteristics of data as a service include:

• Simplified and seamless access to the data
• Accurate and curated data
• Range of data formats
• Data governance happening seamlessly

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Offering data-as-a-service (DaaS) will eventually help in monetizing the data in future as well.

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Conclusion:

The future of data analytics isn't only about adopting an?analytics platform for decision intelligence. The overall landscape is continuously evolving due to the proliferation of new technologies. By adopting the "Data Analytics as Driver" strategy, organizations can build a data ecosystem that can leverage the full power of data to drive actionable insights and informed business decisions.