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6 Steps to build your Digital Engine: Big Data Analytics

Big Data Big Analytics

I have read that the 2 most common reasons for failure are decisions made on Ignorance (or partial knowledge) & ineptitude! And that's true!

The good news is that we are living in a world pushing for Data democratisation (making data accessible for everyone) and therefore knowledge and information is getting easier to access, use and share.

However, data needs a story to be understood as much as a story needs data to make it realistic, understandable and be able to use it for decisions, ranging from simple understanding of consumer ratings for a product you want to buy to data collection to support consumer experience initiatives. Data analytics is not only about analysing data but also transmission, capture, storage, retrieval, link & presentation of such data!

As Peter Sondergaard nicely said it: "Information is the oil of the 21st century and analytics is the combustion engine."

Becoming an analytics-driven enterprise is a multiyear journey, phased to deliver value at an early stage. DATA is the analytics foundation.

So how to build your engine to be powerful, quick, reliable and of course efficient? Follow these steps:

  1. Consumer Analysis: Understand your digital consumers and their needs, their preferred channels, their outputs/inputs. Good questions should identify the specific decisions that data and analytics will support to drive positive consumer impact.

  2. Perform Data discovery: In simple terms, create a data library. IT can be an excellent partner in getting your enterprise architecture repository for the main applications, their use, interfaces and outputs (internal & external data). Translate the IT language applications to business language and bring the two together to build a business model! Most fruitful insights are when you combine transaction data (such as purchase amounts over time), browsing data (including on mobile), and consumer service data (such as returns by region).

  3. Define strategy for 2-3 years:

  4. Create a SINGLE source of truth for data across the enterprise.

  5. Retain data even if not used now. Storage is cheap and it is always easy to get rid of data rather than trying to get historical data.

  6. Ensure flexibility in data structure to allow any change in direction in the future (i.e. unstructured data).

  7. Scale & monetise Data

  8. BUT with quick wins: KEEP IT SIMPLE, focus on important data - 100% solution is never achievable. Sometimes just making data transparency to internal and external consumers that weren't aware of the data is sufficient quick win with huge benefits. Don’t try and change 22 things; try and change 2 or 3 things”

  9. Organizational Transformation: Create simple, understandable tools for people on the front lines. Update processes and develop capabilities to enable tool use. Turn insights into impactful frontline actions.

  10. Build the right team: Get the right bled of:

  • Business analysts: with strong statistical skills to able to extract information of large data, present value to business with a non-analytical business language.

  • Data Scientists / Analytical experts: The brain of analytical mathematical modelling to start moving from "past" reporting to "future" - predictive analytics based on statistical analysis.

  • Data Architects / Technology experts: They translate the mathematical modelling into data models to be implemented in the tools.

  • Consumer Experience / Visualisers: With innovative and design skills, will be able to make the "dull"/"dirty" data look nice and also are the Tool "champions" to bring the enablement of using the tool usage to the entire organization.

As the saying goes: If a picture is worth a thousand words then data analytics & visualisation is worth exabytes bytes with direct positive impact on profitability.

In next 100 days, start with consumer analysis and identify one area that your enterprise or the digital consumer require data transparency on and follow the steps above. It is in the small steps and quick wins where you build the data analytics culture and ecosystem.


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