Let’s begin by naming some iconic duos in pop culture and real-life: Batman and Robin, Fred and George Weasley from Harry Potter, Laurel and Hardy, the list goes on. I’m pretty sure most of them would be good on their own, but with the other, they become even Now, when you look at big data and enterprise data management, it’s essentially the same; they’re both great in their own way, but bring them together and you have a real unstoppable force, so to speak. And in the present day, where social media has taken the world by storm, getting involved just about wherever possible, it is important for companies to harness, if not tame, this behemoth to get the best out of it; both for our sake and theirs. But to understand this, it is imperative to first understand what big data and enterprise data strategy are.
Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. Big data often comes from multiple sources and arrives in multiple formats.
Enterprise Data Management
Enterprise data management is an organization’s ability to effectively create, integrate, disseminate and manage data for all enterprise applications, processes and entities requiring timely and accurate data delivery. It is also a concept that addresses the transmission of different data sets within processes and applications that rely on the consumption of these data sets to complete business processes or transactions.
How does Big Data work?
Big Data falls into two categories:
- Structured: Consists of information already managed by the organization in databases and spreadsheets. Frequently numeric in nature.
- Unstructured: Information that is unorganized and does not fall into a pre-determined model or format.
Big Data can be gathered in any of the following ways:
- Publicly shared comments on social networking sites
- Through apps via questionnaires, surveys, product purchases, and electronic check-ins.
What is an Enterprise Data Strategy?
An enterprise data strategy (EDS) is the exhaustive vision and guide for an organization’s capability to bridle information subordinate capacities. Put simply, it defines the approach the enterprise will take to manage and use its data and information to achieve its business and technology goals, and to realize a competitive advantage using this asset. A good EDS is:
- Practical (simple for the association to pursue when directing day by day exercises).
- Relevant (logical to the association, not conventional).
- Evolutionary (expected to be dynamic at regular intervals).
- Connected/integrated (with everything that comes after it or from it).
Why companies considering big data need an enterprise strategy:
1. Helps set priorities with the existing data source:
The initial phase in designing an EDS is to gather a stock of all data sources, applications, and proprietors. This progression represents the scope and complexity of your information universe and gives the premise to basic leadership. Additionally, it illustrates – to administrators and those managing the data life cycle – where the gaps and competing priorities for assets exist.
2. Rationalizes logical and physical data architecture:
The inventory should empower both business and specialized discussions about the connections between information spaces and potential clashes in definitions/terms. The outcome ought to be a legitimate enterprise architecture that both sides of the venture comprehend and maintain.
3. Provides a guide to eliminating inheritance frameworks:
Your data inventory ought to describe the applications and platforms where data is stored and maintained. It should assist you with understanding the capacities of your frameworks, the measure of exertion associated with continuing day by day tasks and chances to modernize across platforms. Utilize the stock to build up a guide and procedure for modernizing to envision new large information sources and wanted examination abilities.
4.Improves the effectiveness of data quality processes:
A robust EDS 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. Use this tool to reduce inconsistencies, redundancies or gaps in data quality activities.
5. Requires you to assess the data you collect:
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 may introduce vulnerability to regulatory initiatives. Utilize this tool to assess the risk your data exposes you to before you start to expand for new big data sources.
6. Gets rid of the deadweight (unnecessary data):
Working through an EDS 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. 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 EDS. 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. Anticipates the true benefits of big data to enrich existing data:
Now that you have a robust EDS 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.
Building up an enterprise data strategy and metadata management solutions is a significant undertaking that will yield short and long term benefits to any company, of any size, in any industry. Making the data management services, a part of the organization’s business technique will guarantee that information and data accept their legitimate spot as resources that provide multiple advantages.