In the last decade, the Hadoop platform has emerged as a way for businesses to store data in an unstructured fashion. Hadoop delivered a new solution to approach large and unorganized big data sources, giving companies an opportunity to work with more complex data sets.
The open source library allowed for distributed processing and storage of huge amounts of data across clusters. In layman’s terms, it provided a framework for companies to analyze and work with lots of data.
However, utilizing all this data is easier said than done and many IT departments quickly found themselves overwhelmed with the demand to create internal big data analytics projects. What started out as streamlined big data science experiments for IT departments could quickly blossom into major headaches and points of contention.
Something had to change. Many companies recognized data was the key to increasing their success and gaining data-driven advantages in competitive market spaces. A few early pioneers (like Datameer!) began to work on big data analytic platforms that allowed businesses to compile, crunch and analyze their data more quickly and thereby deliver on their SLA more readily.
These big data solutions aimed to reduce the burden on businesses and IT departments while also increasing the depth and accuracy of the big data analyses themselves. In order to maximize effectiveness, the best big data platforms consider the needs and desires of both the business side and the IT side, allowing for deep, complete analysis while reducing the risk of bottlenecks.
The key for big data analytics pioneers was to balance the need for flexibility and customization with the ease of out-of-box solutions. Was it possible to create a big data analytics platform that could be combined with a company’s existing assets and data, while also being substantial enough for new-to-big-data companies to use right away?
The answer to this is yes. Companies that are enjoying early big data analytic success have approached this complex problem with a pragmatic approach to their data and analytic infrastructure. A way that has yielded great success thus far is to buy some and build some big data analytics solutions – a hybrid approach to complex data that delivers a complete view of the business.
So how can companies implement big data solutions? Here’s our simplified overview:
1. First, companies have to take stock of their current assets, an analytic inventory. This is true both for companies with existing big data solutions and companies without, and the assets include systems or applications and staff. The data itself can come from tools like Excel spreadsheets, Google Analytics, invoices, customer complaint logs and numerous other sources.
This analytic inventory will allow your staff to both gain a grasp of the resources at hand and start asking those hard business questions needed to inform, shape and drive your big data strategy.
2. Next, companies can begin rapid prototyping, using their in-house solutions and out-of-the-box systems. This blended approach allows companies to get started very quickly.
3. Finally, your company can focus on the process of converting raw data into analytics. This means producing datasets that allow your company to identify relationships that encourage acting upon and making data-driven decisions. In today’s analytic world, analysts are as interested in asking the next level of questions as the answer itself. This analytic thought process allows the business to become data driven and agile in its approach towards change management.
Of course, the process described above never actually ends. As the ancient Greek philosopher Heraclitus once wrote, “the only constant is change.” This is definitely true with big data. As data pours in, company priorities change. And as new tools become available, you’ll have to adjust your big data approach and revisit your strategies and efforts.
Hence the need for a solution that allows you to grow, evolve and expand business initiatives that involve data-driven insights.