8 Ways a Business Data Strategy Enables Big Data Analysis


By Carol Newcomb, Senior Data Management Consultant at SAS

Whether you are a mom-and-pop donut shop or an international behemoth business, you rely on data to make better decisions. Data is collected and created through daily transactions with your customers. It reflects their buying decisions and preferences, how often they trade and even the prices they are willing to pay.

These days, there is unprecedented focus on managing big data (which includes structured and unstructured data characterized by enormous volume, speed and variation) or developing data sows to store these huge amounts of data. The reason? Big data can provide even better insights into what your customers are likely to want now and in the future – be it medical treatments, a pair of jeans or the latest high-tech smartwatch.

Do you have a corporate data strategy available for managing this invaluable data?

I want big data. How do I get there?

There is no doubt that big data can enrich your analytical potential. That is, provided you have the tools and techniques sophisticated enough to handle these massive datasets. Consider the impact of bringing such unmanageable and truly diverse data sets into your current IT store.

As sexy as the concept of big data is, and as appealing as it may seem, having a solid business data strategy in place to manage your entire inventory of data may not lead to risks in data management and data management. Creating a business data strategy helps control any vulnerability your organization may currently have in relation to data. And it will help you better manage big data when you introduce it into your analytics shop.

What is a Business Data Strategy?

A corporate data strategy is the comprehensive vision and roadmap of an organization’s potential to leverage data-dependent capabilities. It represents the umbrella of all domain-specific strategies, such as master data management, business intelligence, big data and so on.

A good business data strategy is:

  • Practical (easy for the organization to follow when performing daily activities).
  • Relevant (contextual to the organization, not generic).
  • Evolutionary (expected to change regularly).
  • Connected / integrated (with everything that comes after it or from it).

Key reasons why organizations considering big data need a business data strategy

  1. Helps set priorities with existing data source. The first step in designing a business data strategy is to compile 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 shows – to managers and those responsible for managing the data life cycle – where the shortcomings and competing priorities for resources exist.
  2. Rationalizes logical and physical data architecture. The inventory should enable both business and technical conversations about the relationship between data domains and potential conflicts in definitions / terms. The result must be a logical enterprise architecture that both sides of the company understand and maintain.
  3. Contains a roadmap for phasing out older systems. Your data entry must 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 maintaining day-to-day operations, and opportunities to modernize across platforms. Use the inventory to develop a roadmap and strategy for modernization to anticipate new big data sources and desired analytics features.
  4. Improves the efficiency of data quality processes. A robust enterprise data strategy will illustrate the data pressure points for data quality monitoring and correction. This may include data integration points and areas for active data management intervention. Use this tool to reduce discrepancies, redundancies, or gaps in data quality activities.
  5. Requires that you reconsider the data you collect, the value and the risks. Data introduces both value and risk to any organization. There are legal discovery issues to be aware of, and sharing, reporting, storing or archiving data can cause vulnerability to regulatory initiatives. Use this tool to assess the risk that your data exposes you to before looking for new big data sources.
  6. Avoids the burden (and hardware / storage costs) of unnecessary data. Working through a business data strategy should make your business more aware of the total amount of data collected and stored. Part of this awareness comes from documenting key data life cycles, understanding how much data persists in various applications, and determining how long the data is considered viable. What is the plan for big data? How does this fit with existing data retirement practices? What are the associated costs?
  7. Creates decision-making authority for data management 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 a business data strategy. Who is responsible for big data? How are data quality decisions handled? Find out where accountability exists today and where there are gaps. Create accountability mechanisms through your data management relationship and data management activities, and create areas to improve. Then consider the stewardship requirements for big data.
  8. Predicting the true benefits of big data to enrich existing data. Now that you have a robust business data strategy for the current situation, you can start planning where to introduce big data sources to complement analytics capabilities versus where they would introduce risk. Not only do you need platforms and data management resources to handle data volumes; You also need processes and human capital to be responsible for issues that will inevitably arise with completely new types of data.

Get serious about your business data strategy

Compiling a business data strategy (once and continuously) should be a fundamental responsibility of any organization that is seriously looking to use data to provide insight and direction. Even before you introduce big data into a mature, sophisticated IT store, you should anticipate that big data sources are fundamentally different. The differences require careful planning and staffing to ensure that you are prepared for the impact and potential risks when learning how to use big data effectively.



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