With years of evolution in business and technology, data has become the focal point for enterprises. A customer centric approach has made it important for organizations to reach out to their customers in every way possible. What your customers do, where they go, what choices they make, which websites they visit, what attracts them, how they interact with your resources, you definitely want to know every bit of it!
The need to capture all this information about your customers, results in a huge quantum of raw data, generating from varied online like website clickstreams, server logs, social media interaction, paid ads, and offline sources like phone book records, purchase histories, offline marketing campaigns, loyalty card data, demographic data, etc. This variety of data is technically known as Big Data.
But data alone, cannot be of any help. What you need, is to organize this data and derive meaningful insights from it that can assist you in making informed decisions.
What is Big Data Analytics?
While storage and organization of Big Data is one thing, being able to analyze it is an entire different stuff. Big Data Analytics refers to the process of diving deep into the massive pool of Big Data to help you understand your data. It is to study the data over multiple dimensions on a massive scale to uncover the hidden trends of the data, the likely pattern in your customers’ buying behavior, the variations in their preferences, and much more. These business insights and KPIs provide you base for a deeper and better forecasting, budgeting, business planning and managerial decision-making.
Big Data Analytics has the power to provide your business with the capabilities and opportunities to excel and grow phenomenally. But, as intriguing as this concept is, your business is yet to leverage the full potential of Big Data Analytics and the road ahead is not so easy.
Challenges with Big Data Analytics:
The more the data, the smarter your business gets. To be able to know your customers deeply, you need to capture new data every day while at the same time be able to dive deeper into the past. Ideally, businesses use BI tools like Tableau, Power BI, Excel, etc. to analyze their big data. And these tools work perfectly well before the data volume starts increasing.
But, as new data gets added to the storage platforms to go beyond billions and trillions of rows, it generates complexity in analytics and the BI tools fail to perform. As a result, the run time on queries exceeds to minutes and at times hours. Thus, the complex growing data hampers the performance of your BI tools making them unable to generate real-time responses within seconds.
These delayed responses make it difficult for insights to reach the user at the right time. Thus, resulting in delays in decision making. Also, you are restricted by the amount of data that you can analyze and ultimately, you have to compromise on the analytical accuracy of your insights. And that is why, Big Data Analytics is still a pipe dream for many enterprises.
Now to deal with this problem you may want to switch tools, but the sheer comfort of using your existing tool makes such a change way too painful. And so, businesses are often reluctant to switch from their existing BI tools while still having heavy big data investments.
But what if your big data could be analyzed, in its entirety, by your existing BI Tool without hampering their performance?
Bridging the Gap: The BI Acceleration Layer
To overcome the challenges of your existing BI Tools in big data analytics, you need the magic of BI Acceleration! In simple words, BI Acceleration is the process of boosting the power of your BI Tools enabling them to capture and deliver instant insights on massive volumes of data, both on the cloud and on-premise data lakes.
BI Acceleration can be performed by deploying a high performing semantic layer between your data storage layer and the BI tools. This unified semantic layer uses the power of Smart OLAPTM technology to create pre-aggregated, multidimensional cubes of data. These OLAP cubes process and store all possible combinations of your data. So now, when a user fires a query, all the BI Acceleration layer has to do is search the accurate result in the OLAP cubes and deliver it to the BI tools. This reduces processing time and enables BI Tools to deliver valuable insights in sub-seconds.
BI Acceleration Layer, thus, acts like a catalyst in providing your BI Tools with instant access to massive volumes of data removing the limitation of speed and scale. This makes big data analytics a reality for organizations. And most importantly, BI Acceleration helps you bid goodbye to the challenges of big data analytics without having to shift from your conventional BI tools!
