Not Your Father's MDM: Rethinking Your Data Management Strategy
Evolving from traditional to next-gen data management would mean taking these four key steps.
1. Tear Down the Silos Between IT and Business
As per the “State of the CIO 2017 Survey”, 59 percent of CIOs said IT collaborates with business units to build business cases for new MDM (and technology) initiatives, while only 24 percent of business leaders said that was the case. This indicates clear departmental silos between IT and Business. To bridge this gap, you must stop seeing your MDM as a separate siloed discipline, requiring complex IT infrastructure, processes, leading to months of design and implementation. Modern data management encourages an alignment and partnership between business and IT through an extremely user-friendly data governance interface, thus maximizing the ROI of your MDM investment. It has inbuilt collaboration and workflow capabilities to meet your enterprise's governance framework and way of doing business. In addition, it allows IT and business users to provide feedback in a collaborative and controlled manner thus preserving valuable intelligence and competitive advantage.
2. Close the Loop Between Operations and Analytics
According to a recent McKinsey survey, 86% executives reported that their organizations were only somewhat effective at meeting the goals they set out for their data and analytics initiatives. The biggest culprit is a gap between analytics and embedding these insights into the operating model of the larger organization. Turning data into real value requires a profound reshaping of your day-to-day workflow and digitization of transactions and processes to generate and collect all useful data. A modern data management solution helps consolidate and cleanse data from all sources, transform it into reliable data, and provides relevant insights and recommended actions in the context of your operational applications using predictive analytics and machine learning. It allows you to apply analytics to improve the performance of your core operations. It doesn’t stop there and further correlates downstream business actions and results back in an integrated closed-loop, thus converting big data into smart data, providing faster Time To Analytics (TTA), measurable ROI, and better outcomes.
3. Take Polyglot Data Storage Approach to Achieve Big Data Scale as well as Performance
Different databases are designed to solve different business problems. Using a single database for all the requirements usually leads to non-performant solutions. A data-driven application should be able to bring together data from different database types to achieve the business objective. Most of the operational business applications run on relational, columnar databases, but they do not manage relationships well. Graph databases, while suited for uncovering and handling relationships, don’t have the horizontal scalability and agility to meet enterprise needs. This limitation is leading to the emergence of modern data management platforms built on columnar-graph hybrid stores. Once you create data-driven applications on a reliable data foundation of a Commercial Graph, you can visualize all relevant information and relationships as well as quickly pivot from one application to the other. For example, you can see all the purchased products and stores visited in a consumer’s profile, and with a simple click, you can drill into the product profile, roll-up dynamic hierarchical information (revenue, value, product usage) or find key influencers in customer networks.
4. Strike the Balance Between Offensive and Defensive Data Strategy
Retailers who fail to comply with data security can be fined up to 4% of their revenue and lose the confidence of their customers forever. On the other hand, personalization can deliver five to eight times the ROI on marketing spend, and can lift sales by 10% or more. Thus, the need of the hour is to strike a balance between your defensive and offensive data strategies and make considered trade-offs between “defensive” (e.g. security, governance, and compliance) and “offensive” (e.g. revenue growth, profitability, and customer satisfaction) uses of data as illustrated in this HBR article. A modern data management platform offers flexible data and information architectures that involve both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). It focuses on reliable data at scale for "defense" and delivers relevant insights for "offense" from complete contextual 360-degree views, for personalized engagement.
Can you prove the ROI of your data management efforts? Are you able to conquer the gap between your analytical insights and operational execution? Can your business teams leverage reliable data and relevant insights to solve their day-to-day challenges without compromising on scalability and performance? Are you able to implement a mix of defensive as well as offensive data strategies to meet your data security as well as personalization goals? If the answer is no to one or more of these questions, you must rethink (and reinvent) your data management philosophy.