How to become a Data Driven Organization

By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis (Source: Gartner Research)

In 2016, Forrester predicts that customer-obsessed leaders will shift their firms’ strategies to move beyond big data to actually solve problems with data-driven insights.

 Key challenges for establishing a data culture:

• Fear of the unknown:
1. First is the fear of scrutiny because everything is being measured more publicly.
2. Second is the concern that becoming more numbers-focused will cause it to feel like a less personal work environment. Leadership must be able to address these concerns.
• Trust in the data/ data quality - in a data driven culture bad data can lead to unintended consequences where decisions are taken based on wrong information
• Lack of alignment top-down/ bottom-up and cross departmental on how to take advantage of the data

Definition of a data driven culture:

• Treating information as an asset:
• While 80% of CEOs claim to have operationalized the notion of data as an asset, only 10% say that their company actually treats it that way (Source: Gartner 2015)
• Data is the lifeblood of the organization and decisions are not based on intuition or at least accompanied with hard facts based on data insights
• Data is used in every aspect of decision making in the organization on C-Level, management and business analyst level and serving multitudes of data based objectives across departments
• Data is the new currency of the future where no data is disregarded and stored at least initially in the data lake landing zone to be processed further at a later stage
• In a data driven culture you have to be ready to confront the brutal facts: If the data tells you bad news, it should act as a stimulus to drive innovation, creativity and a proactive response
• Structured data is classified and enriched in corporate datamarts for easy self service data consumption
• Unstructured big data is enriched with metadata (e.g. with Hadoop Sentry or HANA Vora) to enable data insights at a later stage through big data techniques including map reduce, predictive, noSQL, statistical and machine algorithm processing approaches

Data driven decisions – How to get from data signals to informed actions:

Successful application of data to decision making creates the need for flexible and scalable cloud and on premise data warehouse infrastructure for data ingestions, storage and processing through a flexible API and web-service fabric layer ease of data ingestion and data integration to other applications and mobile apps.

Operational considerations for data driven organizations:

1. How is the data collected and shared across the organization?
2. What data is being collected vs what data is discarded? e.g. what events of event stream data should be stored at the device vs. data lake level
3. Define how the data will inform business decisions – the alignment on corporate metrics across the organization is key here
4. At what frequency is data needed to make actionable decision?
5. How is the data insight visualized so it can be easily digested, analyzed, drilled into and reacted to.

With all that being said - as with all things "everything is good in moderation" is a principle that also applies here.

Limitations of analysis based on data only:

1. "Analysis paralysis" when approach of focusing on nothing but current business performance metrics can result in a loss to innovate effectively
2. Some things cannot, nor should be measured without seeing the big picture and applying common sense or as Albert Einstein put it:

"Not everything that can be counted counts, and not everything that counts can be counted."

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Disclaimer: The opinions expressed in this post are my own personal views and don't represent the views of my employer.

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