The Quest of Data View House Framework — What prevents a business from being data-driven?
Under the Data View House™ quest, I covered characteristics of data-driven culture in my previous blog. Since the rise of data storage and processing power, initiatives to utilise data for business value have skyrocketed across the industry verticals that desire to be data-driven. However, it was well realised before COVID times that most (almost 80%) of data initiatives fail. Plenty has already been written on why it is hard to be data-driven. I want to add to this pile of information with my unique view on formidable challenges that prevent a business from being data-driven. This point of view is formed over the years with what I observed while working with and on the data.
The root cause of failures in data initiatives is that it is difficult to assign a dollar value to the business data while perceiving it as a business asset. However, going beyond the root cause of the problem, I see the following are critical factors that prevent a typical large organisation from being data-driven.
- Silos of systems with growth.
- 2. Heavy IT Operational cost
- 3. Cost of Integration
- 4. Delays in decision making
1. Silos of systems with growth
An organisation onboards various business systems in its lifetime to meet the needs of different business units such as sales, marketing, operations, HR, etc. Each system is rolled out at different timespan with varying priorities and objectives, creating a web of business data captured and stored in various silos under different ownerships among business units(departments). It is common to find business units hiring or consulting data experts internally to meet their business unit’s reporting and analytical needs rather than liaising/pushing for an enterprise-wide data strategy. A business unit proposing a larger data view for the enterprise has to cross many loops and hops of the management hierarchy, data privacy and security, master data issue, etc., which eventually kills the momentum of the work-stream and results in no outcome. Therefore, one business unit cannot propagate the data-driven culture in the entire organisation.
2. Heavy IT Operational cost
It is common to find legacy systems running the core of business operations for over a decade or two in large organisations. Such legacy systems can have limitations regarding data procurement for analysis and downstream integration, with huge vendor dependency and relative costs for enhancing or modifying anything in legacy systems to meet data-driven objectives. Technology originally made to empower an organisation becomes a growth hurdle and enslaves the organisation, negating the scope for optimisation, innovation and ideas. Secondly, stakeholders see replacing or enhancing such legacy systems at the core of business operations as a huge risk. “If something is running okay, let it run, don’t touch it” syndrome from IT support can also discourage stakeholders from wanting to be data-driven due to uncertainties of business ownership and Return on Investment (ROI).
3. Cost of Integration
Being data-driven involves integrating datasets from multiple business systems to produce coherent business data. This could be a daunting task involving numerous business units and management hierarchies to collaborate. Variations in reference data (customer types, product segments) in different business systems and their poor data quality make integration more challenging and leave the business with only descriptive analytics. (what happened in the past).
4. Delays in Decision Making
The above three factors eventually affect the semi and non-technical decision makers with limited data literacy to be decisive about the business data and undertaking data-driven initiatives. Data-driven is first about the culture’s swiftness, which requires well-planned change management activities. The vagueness of risks and costs associated with data-driven initiatives coupled with different priorities of business units keep delaying decisions on crafting enterprise-level data strategy, data governance and subsequent data initiatives.
Next:
Above is my attempt to briefly summarise the four critical factors I realised that prevent a business from being data-driven. I will cover more about the possible solutions to overcome these stated challenges and get the best outcome and value from the business data. In this journey, the next focus is on simplifying ‘the data as an asset’ jargon.
