Streamline Big Data and BI to Expedite Business Insights

Rajesh Rege, MD, Red Hat India | Tuesday, 20 December 2016, 09:42 IST

While many organizations are devoting resources to tap data both off and online, big data alone will not enable a company to transform itself. To be able to gather and assimilate data is the beginning of a bigger journey. Analyzing that data and making meaningful business decisions based upon it, should ultimately be the  business objective. Big data and business intelligence (BI) together form a closely woven structure. Advanced analytics and BI can be highly complementary; analytics can provide a deeper, exploratory perspective on the data, while BI systems offer a more structured user experience. Companies hoping to take advantage of the promise of big data and BI solutions and stay flexible in a fast-changing environment, are well-advised to embrace open and agile solutions with strong security functionality. 

Big data and BI solutions can not only manage data but also lead to situational analysis and insights. Today’s BI solutions can help users reduce query response times, expand reporting capabilities to mobile users, lower costs, and increase availability and productivity. However, the ability to collect and store massive amounts of data is not a source of competitive differentiation in and of itself. Instead, organizations need to have the ability to quickly analyze and use that data in strategic and meaningful ways to support the business. Organizations should shift their focus from data volume to data velocity. Technologies such as in-memory data stores, which provide far greater speed than traditional disk-based databases, can be paired with processing engines to help applications collect, analyze and respond to business opportunities as they happen. This can contribute to a more agile and effective organization that is better able to address competitive pressures.

Why firms haven’t been able to implement a Data Lake yet?
A simple answer to this question is cloud computing. A data lake needs a repository capable of storing vast quantities of data in different formats. To store a vast quantity of data, a robust cloud infrastructure can offer the needed flexibility and scalability. Users should be able to easily search and explore structured, unstructured, internal, and external data from multiple sources in an environment that can meet their security and management requirements. Making data lakes more accessible via a cloud - whether private, hybrid or public - may be key to successfully implement data lakes.

Recent innovations in machine learning are certainly interesting. Humans, as we know, are intuitional beings but machines used to lack such capabilities. With the advancement in machine learning, machines are now able to make decisions more intuitively. I believe these are early stages in artificial intelligence, but the advancements are promising. Machine learning can identify patterns that humans may overlook and which can be hard to notice in the age of big data.

Some early adopter organizations are using machine learning to automate rudimentary tasks and that’s the basic beginner’s puzzle to solve before researching bigger problems. An algorithm can make moves based on knowledge previously "learned" by machine learning, through a deep learning method resulting from extensive training, both from humans and computer play.

Today, there are many practical applications of machine learning on big data. Financial institutions can more quickly detect fraud. Utilities can systematically predict failures and perform prescriptive maintenance. Retailers can mitigate customer turnover, and anticipate consumer purchases with higher accuracy. As machine learning advances, enterprises may be compelled to take a closer look at how this technology can impact their business.

Accelerate Time to Insight and Action
It all starts with selecting the right partner to help enable your digital transformation. The right solution can help reduce transition time, and business analytics can aid in gathering the data and delivering them to the concerned party. 

The key is to capture data, understand it, and then anticipate circumstances to act on it. An ideal partner should be nimble, agile and importantly open source and open standards. And I say this not because of Red Hat’s philosophies, but the importance of not being locked into one partner throughout your transformation. For real innovation, companies should be agile and open source tools and standards can enable that. Increasingly, companies need to use enterprise data in real time to keep pace with competition, meet customer expectations, and adapt to changes in the market. With the right data platform and tools, enterprises can make better informed decisions and accelerate time to insight and action. 

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