There is a lot being spoken about today on Big Data analytics which is a very ‘Hot’ topic of discussion in every industry forum but one fundamental question that many organizations are still struggling to answer is – Where do I start?
While there is no right or wrong answer to this question, there are various strategies and plans that different organizations adopt to gain value from applying predictive analytics of which some achieve tremendous success and some fail miserably. Typically these can be broadly classified into two approaches: Bottom-up or Top down.
In the Bottom-up approach aims at first laying the foundation in terms of data requirements, data access from disparate source systems, data transformation, managing quality, designing a data warehouse, etc., that will support a myriad of pre-determined Predictive/Big Data use cases. Such an approach is mostly led by the IT organization with backing from the business.
On the contrary a Top-down approach is a bit more radical in that you may decide to just select a use case that a business stakeholder within the organization is willing to sponsor and act as a ‘guinea pig’ to test the hypothesis in a Proof-of-Concept (PoC) environment and then convert that into a larger initiative if the success criteria for the PoC is met. This approach mostly is driven by the business with support from the IT organization. I remember one of the key stakeholder in a large Utility organization where I have been involved explaining this to his team as ‘I have bought a solution for you. Now find me a problem to solve’.
If your organisation is just starting on this journey you may decide on any approach but you need to make a start somewhere to take advantage of technology and concepts in the area of advanced analytics and derive value from the vast amount of information/data owned by the organization. To help you in this process I would suggest a simple 3 step approach I call ‘The 3i Framework’ for Big Data analytics.
Information – In this step you try and gather knowledge about what tools, technologies & solutions are available in the market that can support a Big Data analytics initiative within your organization. This may involve some research and reaching out to your technology partners for consolidating the information. This will help you to conduct a ‘Gap-Analysis’ in terms of what you currently have and what you may need in case you wish to proceed with such an initiative.
Inspiration – This step would entail you to look at what some of your peers (who are already ahead on the analytical maturity curve) within the same industry or business function are currently doing in the area of Big Data Analytics. You may need to study the different use cases and how they have been leveraged to drive business benefits. This would give you a very good understanding of ‘The-Art-of-The-Possible’ in the area of Big Data and how some of these use cases may apply to your specific organization.
Innovation – This step needs some ‘soul searching’ exercises that you will need to drive within your own organization through focused group discussions and brainstorming sessions to determine what are your current business pain points and finding out a list of Big Data analytics use cases that can help address these challenges. This ‘Use Case Shortlist’ should give you a good starting point for embarking on your advanced analytical journey in driving transformation through innovation within your business.
Are you thinking about starting a Big Data initiative in your company and don’t know where to begin? Have you already tested the waters but need some inspiration to know what the possibilities are? Do you want to learn more about SAP tools, technologies & services in the area of advanced analytics and how they can help you drive business value? Then join me and my colleagues on the 8th of April 2014 at the SAP Conference for Utilities to find out more.
Join me at the pre-conference Workshop 4 ‘Predictive Analytics Strategies for Utilities by Leveraging Big Data: Does Size Really Matter?’ from 9:00 AM to 1:00 PM on the 8th April 2014.