What and why of Big data analytics: Part I

Smarter technology has paved the way for the instrumentation of just about everything from cash machines, superhighways, oceangoing vessels to refrigerators. Mobile technology has become pervasive, irretrievably ingrained in the way we live and work. The results have been life-changing, smarter cities, more productive workforces, healthcare advances, education reform, and have ultimately created a smarter, more interconnected planet.

The by-product of this global transformation has been the most significant aggregation of data in history. Nearly infinite in its variety, staggering in its volume and often overwhelming in its incoming velocity, it’s earned the right to its name, big data. It’s online, in the news, and found a place in our conversations. Type in “big data” into a search engine, click surf, and you’ll get more than a billion results.

Yet, the concept of big data has been around for years. But today most organizations have come to understand the significant value they get from capturing and applying analytics to all the data that streams into their businesses. Now, even back in the fifties, long before anyone spoke of “big data,” companies used basic analytics, that were manually examined in the form of numbers in a spreadsheet, to uncover trends and insights.

What is big data?

You’ll find that big data means different things to different people when you ask that question in business circles today. The size of your business, your role, the context in which you acquire data, how you put that data to use all factor into your definition of big data. However, for the sake of simplicity lets say big data represents the structured and unstructured data moving throughout the organization from inside and outside the firewall. It’s structured internal data like processed transactions, customer information, and receivables and payables information. And it’s unstructured data elements like emails, social media posts, and customer reviews. Basically, it’s everything from ATMs, instrumented data from sensors, cell phones,  GPS signals, web clicks and so much more.

Big data is really just large amounts of data near real time or in real time from various sources that today probably don’t talk to each other, offered up to the business for analysis. Now “big” is of course, relative to the size of the organization. It typically just means bigger data than the company had wrestled with in the past when most data was highly structured and for the most part, quantitative in nature, in the form of spreadsheets or tables. And while a large business might not think that two terabytes are all that big, for a mid-size organization this might be huge. All of it depends on the company’s frame of reference. So now let’s water the definition down to its simplest form, big data is more data than your company is used to. Leveraging data through business analytics can drive smarter decision making, optimize business outcomes and improve flexibility and agility, no matter the size of your organization.

What is big data analytics?

Big data analytics is the use of processes and tools to glean insights from huge amounts of data. This data is either characterized by high velocity, large volume or extreme variety. From this data, that was previously beyond our reach using old tools like spreadsheets. Big data analytics aims at deriving conclusions and correlations. This is done with the help of tools like SAS, Hadoop, R, etc. which are far stronger than our columns and rows.

The advantages of using big data analytics

The major benefits big data analytics brings to the table are efficiency and speed. Today, thanks to this technology, businesses are able to identify insights for instant decision-making, this ability to stay agile and work faster gives organizations the competitive advantage they lacked before. The value it provides is discussed in more detail below:

• Cloud-based analytics and big data technologies like Hadoop bring remarkable cost benefits when it comes to storing huge volumes of data, in addition to identifying more efficient methods of doing business. Processes like testing and quality assurance could involve many complications especially in industries like nanotechnologies and biopharmaceuticals. Big data analytics will give you insights on the impact of different variables in the production process, thereby helping industries take better decisions.
• With the in-memory analytics and speed of Hadoop, compounded with its ability to analyze new sources of data, businesses can analyze information instantly and make faster decisions based on what they’ve gleaned. In fact, big data analytics analyzes past data to make predictions. This way organizations are prepared not just for the present but also for the future, giving them a competitive edge and providing a more agile framework for risk handling and decision making.
• With this ability to gauge customer satisfaction and needs through analytics comes the power to give customers what they desire. With big data analytics, more companies are working on new products, with a lot more confidence in the products ability to meet customer needs.

The challenges associated with using big data analytics

• Segmenting useful data from clusters is the biggest challenge when it comes to implementing big data analytics.
• An absence of internal analytics skills and the major cost of hiring good data engineers and data scientists to plug the gaps are some of the potential pitfalls that can trip up organizations on big data analytics initiatives. Add to it, the lack of talented personnel who have the skills to make sense out of big data. From budgeting to strategizing and from recruitment to training, using big data analytics comes with a fair share of possibilities as well as challenges.
• Additionally, the use of various data stores and platforms in a big data architecture could result in data silos.
• Integrating Spark, Hadoop, and other big data tools into a well-integrated architecture which meets a company big data analytics requirements is a challenging task for many analytics and IT teams, who have to figure out the right blend of technologies before piecing it all together.

Even with so many challenges ahead of us, we must give this tech the chance it merits.