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Your Guide to the Big Data Analytics Tools Trends and Best Practices

Expert.ai Team - 18 November 2016

As more and more companies are leveraging big data analytics to improve their ROI, this post is your guide to the big data analytics tools, trends and best practices that you need to know.

According to IDC, the big data analytics market is growing rapidly, and this trend is expected to continue in the coming years. In fact, in its Worldwide Semiannual Big Data and Analytics Spending Guide, IDC estimated that worldwide revenues for big data and business analytics (BDA) will grow from $130.1 billion in 2016 to more than $203 billion in 2020. That’s 11.3% growth in 2016 after 2015 worldwide revenues of $122 billion with a growth rate of 11.7% CAGR expected to continue through 2020.

Big data analytics tools trends

Organizations in every industry are struggling to manage their knowledge, mostly in the form of unstructured information. Because the traditional enterprise search-centric approach no longer works, they are looking for solutions that are able to make sense of the tremendous volumes of text-based documents and reports, email, social media information, customer intelligence, etc. that they manage.

The relentless growth and availability of data based on unstructured text is driving demand for a new generation of technology and big data analytics tools. Organizations are deploying cognitive technology and artificial intelligence solutions into almost every kind of application and process. And this relevant trend is confirmed by numbers. According to IDC, spending on cognitive computing and artificial intelligence across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020 (Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide – IDC Study Description; Worldwide Cognitive Systems and Artificial Intelligence Revenues Forecast – IDC Press Release.)

The ability to understand enterprise data using artificial intelligence and cognitive technology is automating business processes and a broad range of strategic activities. In fact, big data analytics tools that leverage a more intelligent approach, such as one that mimics the human ability to understand text and language, help companies gain greater value from their data.

Big data analytics tools best practices

A successful big data strategy starts by identifying your business goals and best practices:

Focus on the right data and not only on the size of data: Since you’ll want to get the maximum business value from your data, you will need to incorporate both structured and unstructured information in your analysis. Don’t start with just everything, but take your time to carefully choose what you want to analyse based on your business goals.

Center on a data-driven decision-making approach: It is important to establish specific rules and include data owners in the analytics process to make sure that you have the right output. Once the raw data has been turned into actionable findings, it is also important to rely more on data and less on personal deduction. While experience, personal skillsand even instinct may play a relevant role later in the analytics process, at this stage, you’ll want to rely more heavily on the data.

Support collaboration between teams and departments: Since the data ideally incorporates different areas of your business, a collaborative approach between the teams and departments involved usually supports a better big data analytics process.


What are the best big data analytics tools? Industry analysts research and studies can be a good starting point for organizations that are thinking about big data tools techniques and strategies. For example, Forrester, one of the most influential research and advisory firms in the world, recently identified the 10 most significant text analytics platform providers in the in “The Forrester Wave™: Big Data Text Analytics Platforms”.

In our opinion, the best big data analytics tool is the one that matches your specific business goals. And even if there is no one way to ensure big data analytics success, following a few best practices can help organizations improve their skills and results. Today, cognitive systems and artificial intelligence solutions are considered top big data analytics tools because organizations are being increasingly deployed in a variety of industries to improve access and management of unstructured information.