Have you read Forrester’s 2016 predictions for business? Analytics is clearly indicated as a core competitive weapon your business needs to be successful and win. Forrester correctly emphasizes that analytics should be on the top of any CXO’s agenda in 2016.
I could not agree more. Let’s discuss why this is so important.
In 2015 Big Data had the momentum that we had foreseen. Big Data initiatives gave us a broader range of data, a range that brought additional data from two directions:
- Access to more levels of detail – In-memory computing allows for immediate access to archived data for analytics. In-memory computing also allows you to present the full level of detail to end users because of its ability to handle the processing and calculations that come with the huge volumes.
- Access a broad scope of data – This scope might include connected networks, sensor data, or social media metrics.
Does all this really seem like good news? Yes, it is good news, but it also brings some challenges that aren’t fully under control today. It assumes you are able to get value from your new data. It assumes organizations are capable of analyzing and acting on the new data that can put them ahead of the competition. Big Data is useful, of course, but only when you can access, understand, and do valuable things with it.
This is exactly the point where analytics becomes either a competitive weapon or just a commodity. Getting valuable insights from Big Data is the ultimate goal. Business analytics is the sole way to achieve this, and that’s what the Forrester report is talking about.
The issue with Big Data
So what issues do organizations face?
The amount of Big Data – both in volume and in scope – increases so intensely that organizations can face challenges to quickly get value. Unknown data is coming to you, and you need to understand what the data is about, where it is located, and where it is coming from before you can start analyzing it and searching for insights. If you have answers to these questions, it should be possible to easily access, explore, and interact with the data at reasonable response times. When necessary, you may require additional resources to search for data correlations and unknown patterns. Depending on the outcome, you might want to involve others to discuss if the new data is of use or if it needs to be further enriched. This could affect ETL processes, for example.
I believe that at a minimum, a core base of business analysts is necessary to document, explore, and maintain core flows of new data. These analysts can advise business users what structured and unstructured data is available, where it is, how to access it, and how it relates to the corporate data that is already available to users.
Prerequisites to utilize Big Data with business analytics
- In-memory computing for business analytics
In order to process, calculate, and analyze the maximum level of data, in-memory computing is a must. In this article, I described the business cases for in-memory computing for business analytics. In-memory computing is the only method that allows you to interactively explore the new data, find correlations, and create valuable insights.
- Core base of business analysts
A small team of analysts can act as a gatekeeper for new structured and unstructured data, informing users where it is and what it is about. This team ensures that new data is compliant to company standards, secure and governed.
Working with new data that is structured or unstructured means the analyst needs to be able to interact with it. Explore, filter, exclude, calculate, enrich, correlate, and visualize: These activities should be possible on the fly with a decent response time. It means you both need the calculation power as the tool capabilities. The very useful BI Component selection tool can help you decide which tool is best.
- Predictive analytics
New structured and – definitely – unstructured data is analysed, looking for patterns and correlations that help you better position against your competitors or find new ways to serve your customers. The business analysts working on Big Data require predictive capabilities in their tools. Further, they must use predictive capabilities without needing statistical background knowledge. The latest predicative analytics tools are capable of doing this while respecting R algorithms.
- Continuous feedback loop
New data means new insights if you can fulfill the above requirements. Your new insights might need to be incorporated into existing insights. With this you need a constant feedback loop to your Business Intelligence Competency Centre (BICC).
These prerequisites are a must if business analytics, as predicted, is to be the competitive weapon for business in 2016. The prerequisites are not complex and can be implemented in any organization. If implemented well, business analytics will help you you to get ahead of your competitors, find new ways to serve your customers, and even find new ways to approach business activities.
I am looking forward to 2016 – the future is ours!
For more insight on how data analytics can boost your business, see Analytics: The Engine Room Of More Successful Sales And Marketing.