The importance of data in running a successful business cannot be overstated. To stay ahead of the competition, optimize operations, and unlock growth, organizations are willing to invest in a data strategy that aligns with their business goals. However, the question arises: How do you develop a data analytics strategy that can truly drive your business forward? Here’s a five-step plan based on real-world experience and engagement with large enterprises:
Step 1: Unite Stakeholders around a Shared Vision
In large enterprises, data silos can form naturally due to the sheer volume of business operations. Different stakeholders may have ownership of these silos. To develop a data management strategy, the first step is to get buy-in from all stakeholders and unite them around a shared vision for enterprise-wide data management. This requires significant effort and top-down support, but it is critical to answering two essential questions:
- Who owns what data?
- Who is accountable for it?
With these questions answered, your organization can drive the data management strategy forward with purpose and clarity.
Step 2: Identify and Leverage All Data Assets
Gone are the days when data assets were static and well-defined. With technological advances, new data points are becoming more pivotal, and organizations must keep digging to uncover data that impact their business. While internal data points are still critical, external data points are increasingly becoming relevant. External data can be related to social trends, economic indicators, competitor data, and regulatory data, among others.
The discovery of data assets is an ongoing process that requires continuous focus to keep pace with the changing dynamics of the world.
Step 3: Build a Robust Data Supply Chain
Now that you have identified the data assets and stakeholders, it's time to build a robust data supply chain. This is the most critical step in your data strategy as it will drive the future state of your organization. The data ecosystem you build should be agile enough to onboard new data sources quickly, deliver data seamlessly, and meet all business SLAs. It should also be able to support structured and unstructured data assets and all technology advancements.
To build a strong data supply chain, consider adopting cloud solutions, which require less investment and offer more agility than on-premises solutions. Make sure you have a clear understanding of the data flow, from the source to the final output. Your data supply chain should include a data lake, data warehousing, ETL processes, and data quality checks.
Step 4: Your Data
Governance is a critical aspect of any data strategy. Once you have established a successful data supply chain, you need to build a culture of data governance. Governance is not just a technology problem; it requires investment in people and processes. Your governance program should include a data stewardship program, metadata enrichment, data quality measurement processes, regulatory compliance, data security and privacy, and data democratization.
Ensure that your governance program is ongoing, with iterations and continuous improvements based on the addition and discovery of new data assets. Make sure all stakeholders understand the importance of governance and are trained to adhere to it.
Step 5: Offer Data-as-a-Service (DaaS) to Your Consumers
In this agile world, consumers don't have time to wait for IT teams to deliver data according to their requirements. Offering data-as-a-service (DaaS) can help you solve this problem. DaaS allows consumers to register for a service to access 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, machine learning model, data analyst, data scientist, downstream application, or external entity with access to certain data points.
The key characteristics of DaaS include simplified and seamless access to the data, accurate and curated data, a range of data formats, and seamless data governance. Offering DaaS can eventually help you monetize your data in the future.
Conclusion:
By adopting this five-step data analytics strategy, you can build a data ecosystem that harnesses the power of data to drive actionable insights and make informed business decisions. Building a strong data supply chain, discovering data assets, bringing stakeholders onboard, governing your data, and offering DaaS to your consumers can help you achieve your organization's growth goals. Remember, data is not just a tool for decision intelligence, but it's the foundation of your organization's future success.