Must-Know Data Strategy Priorities for CIOs
Today’s data strategy revolves around four key initiatives, including data democratization and data orchestration.
- By Rajan Nagina
- February 6, 2023
Data is the essence of any business operation, whether used to make informed decisions or to drive growth. However, having a massive collection of data scattered across departments is not enough. The real game-changer lies in how you use and manage this data, and this is where data strategy comes in.
Data strategy is the foundation on which data management and its dimensions rely. Any advancements in this area, from the basics of data governance to advanced analytics, have a direct positive impact across all business functions.
The best data strategy is driven by four key priorities: data democratization, data orchestration, a drive-to-market strategy, and risk management.
Priority #1: Data democratization
It’s no longer enough to make data and analytics available to more users. An ever-growing number of jobs now rely on data interpretation, especially when looking at data in new and innovative ways. If human interpretation isn't needed for a task, an algorithm can (and will) usually be put in its place.
With access to new tools that make it easier to explore and manipulate data, employees can move beyond basic reporting and analytics to finding and explaining anomalies more quickly. As a result, the workforce is becoming increasingly data literate and can work without support from data specialists.
As data literacy becomes more common in the workplace, employees are expecting (and sometimes demanding) greater access to more data. A successful data strategy addresses this by focusing on transparency and accessibility.
Priority #2: Data orchestration
We are at the crossroads of technological innovation. Businesses that were at the front of the digitization race earlier now have siloed data systems with multiple on-premises storage locations. To be able to advance toward data science and AI, they need this data to be combined and organized centrally. A centralized data architecture for an enterprise helps automate and streamline data-driven decision-making and supports technologies that work in that direction.
Data orchestration provides a way to easily bring data together from multiple sources, standardize it, and prepare it for analysis and insights while ensuring data security, privacy, and governance.
Data orchestration from siloes to a centralized system helps tackle some critical issues such as complying with data privacy laws, removing data bottlenecks, and enforcing data governance across the organization, helping to achieve data democratization.
Priority #3: A drive-to-market strategy
Every organization has the know-how to maintain data directly related to its business growth. However, an effective data strategy must adapt quickly to market changes. A modern, flexible, and interconnected data platform must be similarly agile to shift focus quickly as priorities and business conditions change.
This adaptability needs to be applied to two aspects of data strategy -- data management and data organization. Adaptability in data management is largely related to the tools in place for data provisioning. Data management tools need to support the addition of new data sets and new consumption-layer applications in the future.
Data organization needs to be inherently adaptable and flexible as well. Sets of data domains relevant to the business must be identified and a governance team must be organized around them. The relevance of the selected domains evolves as the market strategies of the organization change. Data governance and organization need to identify what data will be important in the future and how the system can adapt to the changes.
Priority #4: Risk management
Data governance is no longer just about compliance; it's about using data to support and improve business strategies. A formal data governance structure is needed to make the most of limited resources, with clearly defined roles and responsibilities. This includes having defined data domains based on business topics (such as “Customer”) rather than just where the data is physically located (for example, a CRM database). This way, you can keep track of all the data more efficiently and ensure compliance with government regulations and industry standards and mandates.
As data becomes more valuable, it's crucial to have a solid understanding of how to manage the associated risks. For example, customer contact information or personal identification information must not be stored in a form that can be retrieved and used for unauthorized or illegal purposes. Besides, there are government regulations for every country that provide a set of guidelines about how to handle user data.
Working closely with the legal and compliance teams can help you assess and manage these risks. This will ensure that your data engineering strategy aligns with your company's overall risk management strategy.
Business leaders still working on an inside-out approach that starts with their products and services and ends with customer purchase need to rethink their entire modus operandi. Instead, the focus should start with the customer's requirements, an outside-in approach.
Clarity about business goals is crucial to effectively leverage state-of-the-art technologies such as artificial intelligence, machine learning, and deep learning. Because data is the critical currency for success, this shift of perspective will pay off as a competitive edge in the market.
Rajan Nagina leads AI practice and is responsible for the AI business at Newgen Software. He has 20 years of experience in product management, business development, and sales. He is co-founder of Number Theory, a low-code data science platform recently acquired by Newgen. He is passionate about democratizing Enterprise AI as he believes all leading companies will be AI-first companies in the coming time.