What is Your Company’s Big Data Maturity Level?

By: rajeev | February 20, 2017
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Every business is generating massive amounts of data, much more than they can handle and analyze. Big Data helps companies integrate their data from various disparate sources and analyze it to gain intelligent information about their business. This intelligence can be applied to minimize risks, maximize revenue and reduce operational costs. The deep insight into business historical data helps see the correlation between various aspects of the business and allows educated decision-making. In this blog, we will discuss about companies that have identified the need for big data and have already started the incorporation. Big Data is not a one day or a man job to start and complete. It is rather a journey that a company starts for a better and brighter future. However, analyzing your big data analytics adoption progress is as important as Big Data analytics itself.

Why Big Data Maturity Test?

It helps you understand where you stand in your Big Data strategy roadmap and also helps compare your big data deployment from your peers. This knowledge helps in improving the Big Data adoption process and ensures best-class insight and support. There are many models in the market that can enable an organization to assess their Big Data maturity level. Below is the break-up in different stages:  

  • Early or Inceptive Stage: This is the phase when the company starts to understand its need for Big Data and consequently, starts preparing for Big Data adoption. Some companies deploy Big Data solution to tackle any particular problem, for example, expenses overtaking revenue or the realization that data is their biggest asset to maximize productivity. Then there are others who simply cannot function smoothly using their current data infrastructure and have no choice but to adopt big data.
  This stage involves only a few people thinking about Big Data adoption and trying to find out its real advantages for the business. At this stage, organizations do not have a data-driven strategy and lack the understanding about the importance of data.  

  • Groundwork: This phase involves preparation for adopting Big Data. Organizations usually partner with a Big Data Analytics technology company and also invests in technology like Hadoop or NoSQL and experiments with it to create a Proof of concept. The adoption is usually centered on some particular business problem or department. Data sources are identified and ways to define and gather data are deployed. There is a governance committee responsible for tracking and measuring the big data strategy adoption with the new business partner.
  The organization is doing its homework and getting all gears in function for Big Data adoption, however, the real progress starts in the next stage.


  • Business Adoption: By this stage, the organization has established one or two Proof of concepts (POCs) that are production ready. The scope of adoption is still departmental but more people get on board with the idea of using Big Data enterprise-wide. Different kinds of businesses have their own unique challenges and need for Big Data. For example, a publishing house maybe storing and organizing data in proper data sets and formats, however, they may not have any analytics strategy or model to take advantage of their data. Another kind of company might be already using some sort of predictive analytics method but lack a proper centralized data storage.

  This stage incorporates Big Data in its infrastructure either deploying it on cloud or on-premise. Data architecture gets more defined and decision making improves with analytical reports.    
  • Enterprise-wide Adoption: With solid business case and Big Data roadmap, the organization is now convinced about corporate adoption. This may require investment in infrastructure, technology, skill set, and resources. However, the business needs Big Data for competitive advantage and is willing to use Big Data as an investment rather than an expense. An enterprise-wide change is a huge step and involves personnel management, data management, life cycle adoption of Big Data and if required, cloud adoption.
  Internal employees need to be trained for the use of reporting techniques. This stage completely changes the way a company runs its business. New opportunities are explored and challenges are discovered. Visualization techniques create stunning dashboards that give deep insight into the historic trends and also helps is future predictions.    

  • Truly Mature: Big Data is the biggest buzz word in the business industry and yet there are very few players who have actually adopted it thoroughly. So there is a small percentage of companies that are in this stage and are truly Big Data mature organizations. Companies in this stage are through all the initial hick-ups and preparation and are functioning like well-oiled machines with Big Data at their backend.

Data silos no longer exist and instead, the data from every department is stored at a centralized repository and is accessed with different permission levels identified in the fourth stage. The mission-critical aspects of the business are well aligned with Big Data and are using advanced analytics to break complexity and work in a matured environment. These five stages define the maturity level of an organization using Big Data in its data management, infrastructure, analytics, and governance. These are the key characteristics of Big Data adoption lifecycle and they keep changing with each stage.

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