How To Enable Data Intelligence in Any Organization
Eight reasons why organizations need an enterprise data strategy, that is practical, relevant, evolutionary and connected.
No doubt: big data can enrich a companys analytics potential. That is, assuming the tools and techniques are sophisticated enough to handle these massive data sets. As appealing as the concept of big data may seem, not having a solid enterprise data strategy in place to manage your entire inventory of data leads to risks in data governance and data management. "Establishing an enterprise data strategy will help control any vulnerability your organization may currently have relative to data", says Kim Kaluba, Senior Product Marketing Manager, Data Management, SAS. Here are eight reasons why an enterprise data strategy is a must:
1. A data strategy sets priorities and identifies possible data and sources that can be used to support the corporate initiatives
The first step in designing an enterprise data strategy is to collect an inventory of all data sources, applications and data owners. This step illustrates the scope and complexity of your data universe and provides the basis for decision making. It also demonstrates to executives and those responsible for managing the data life cycle where the gaps and competing priorities for resources exist.
2. A strategy rationalizes logical and physical data architecture
The inventory should enable both business and technical conversations about the relationships between data domains and potential conflicts in definitions and terms. The result should be a logical enterprise architecture that both sides of the enterprise understand and maintain.
3. It provides a road map
Your data inventory should describe the applications and platforms where data is collected and maintained. It should help you understand the capabilities of your systems, the amount of effort involved in sustaining daily operations and opportunities to modernize across platforms.
4. It improves the effectiveness of data quality processes
A robust enterprise data strategy will illustrate the data touch points for data quality monitoring and correction processes. This may include data integration points and areas for active data stewardship intervention. Using data quality tools reduces inconsistencies, redundancies or gaps in data quality activities.
A Successful data strategy supports the corporate strategic initiatives.«
5. A strategy requires you to rethink the data you collect, the value and the risks
Data introduces both value and risk to any organization. There are legal discovery issues as sharing, reporting, storing or archiving data may introduce vulnerability to regulatory initiatives. Use this tool to assess the risk your data exposes before you start to ramp up for new big data sources.
6. It can avoid the burden of hardware and storage costs of unnecessary data
Working through an enterprise data strategy should make your enterprise more aware of the total amount of data collected and stored. Part of this awareness will come from documenting key data life cycles, understanding how much data persists in different applications and determining how long the data is considered viable.
7. A strategy establishes decision-making authority for data governance and data management
A thorough analysis of your existing data universe should include an assessment of accountability and ownership for each data source and application. This is a critical part of an enterprise data strategy. Who will be responsible for big data? How will data quality decisions be handled? Find out where accountability exists today, and where there are gaps. Establish the mechanisms for accountability through your data stewardship and data governance activities and shore up areas that need improvement.
8. It anticipates the true benefits of data
Now that you have a robust enterprise data strategy for the current state of affairs, you can begin to plan for where you should introduce big data sources to supplement analytics capabilities versus where they would introduce risk. You’ll need not only the platforms and data management resources to handle volumes of data; you’ll also need the processes and human capital in place to be accountable for questions that will inevitably arise with entirely new types of data.