Whilst terms like “Big Data”, “Data Analytics”, “Business Intelligence” and “Data Science” have seemingly being around for many years, not a lot of companies have really understood the boundaries between these, and the interrelationships between them to lead their efforts in data to genuine business impact.
Business impact is the key end goal from any investment in data initiatives in your company. Whilst data exploration is always a useful exercise, if it does not lead to benefit for either your internal organisation or your customers, then it can be a waste of company resources.
Although the specific approach to the application of analytics – either through BI, Data Science, or application building – may vary according to an enterprise’s needs, it is important to note the broad applicability of BI. Its capacities are constantly expanding to include greater access to more forms of data in intuitive, interactive ways that favor non-technical users. Consequently, the business can do more with the data accessed through these tools in less time than it used to, which makes applying discovery-based BI an excellent starting point for the deployment of analytics. A nice approach outlined by Michael Li of LinkedIn here, shows an EOI model for driving business value.
According to Gartner: “By 2015, ‘smart data discovery,’ which includes natural-language query and search, automated, prescriptive advanced analytics and interactive data discovery capabilities, will be the most in-demand BI platform user experience paradigm, enabling mainstream business consumers to get insights (such as clusters, segments, predictions, outliers and anomalies) from data.”
Data Transformation is key
Companies around the globe normally have these questions to answer: Just where is all my data? What format is it in? Can I use it? A large amount of the challenge is maximizing the business impact from your data is to understand what I like to call your “Data Atlas”. And it is normally a journey. The larger the company, the greater the size of this challenge. Multinationals for example, being in existence for a long period of time have offended for a longer period of time, with it common to have multiple data centers, hosting strategies, database types, data types, data format, and how the data is actually used. It can be difficult for these companies to get their data into the formats required for the latest data software platforms. This can be a time consuming exercise, which can
Looking at the industry, one company that is doing wonders in solving this type of challenge for companies is Analytics Engines, based out of Belfast. Their “Fetch Do Act” methodology offers a click to deploy, end to end big data analytics platform that enables rapid transformation of your data into business insights within a single environment. Check it out here. This major advantage of this approach is that it accelerates your data transformation, so you can focus more of your time on the “Act” element. Remember Big Data is just a tool.
Defining Data Science?
Explore. Hypothesize. Test. Repeat.
That’s what scientists do. We explore the world around us, come up with hypotheses that generalize our observations, and then test those hypotheses through controlled experiments. The positive and negative outcomes of those experiments advance our understanding of reality. Now one of the best definitions for Data Science I have come across is described by DATAVERSITY™ as:
“Data Science combines the allure of Big Data, the fascination of Unstructured Data, the precision of advanced mathematics and statistics, the innovation of social media, the creativity of storytelling, the investigation and inquiry of forensics, and the ability to use all of those skills together while still being able to demonstrate the results to non-technical audiences.”
Just like in any other Science industry, everything you do with a sample, whether it be biological, chemical or physical, is considered science. Up front analysis, sampling, applying statistics, interpreting and securing the end results.
Beware the Hype
Industry indicates that the hype curve of analytics has peaked, but as it settles, terms like machine learning and predictive analytics are coming up the hype curve, and will have a huge role to play in the coming years. But ensure you only adopt them when the use cases require them. See past the buzz and ensure your strategy takes on board industry trends, but is somewhat unique to the personality of your company. Stay focused, and ensure simplicity is at the forefront of your mind. It is also becoming easier to outsource and partner on some of these advanced methods, typing “machine learning platform” into Google will give numerous results (here).
Customer Centric Analytics
Exploration and experimentation is an important part of your data journey. The key is not to let it become all you do, and to understand the difference insight and impact. Insight does not result in improvement unless you can translate it to business impact. The “data to action” loop below does a nice job of visualizing the difference between data to insight and insight to action.
Know your customer. Every data custodian has one. The IT Manager’s customer is the Data Architect, who’s customer is the Data Scientist. They in turn must ensure they meet the requirements of the business sponsor, and having a use case to solve or KPI to meet will help you to build comprehensive return on investment (ROI) statements, and ensure a quicker acceptance in the importance of analytics in your companies business future.