Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail | 7wData

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We examine the main reason for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.

I have been working as a Data Science Professional for last 11 years and had the chance to engage with a number of employers, clients for their data science needs both as an employee and entrepreneur across multiple domains starting with Financial Services, Retail/FMCG/CPG, Telecom, Media and Entertainment, Digital Media, Education and Technology etc. During the last 11 years, I had the chance to observe and participate in the management practices and enterprise strategy with regards to data science and have closely observed the success and failures of those initiatives. I look back and reflect on the top 5 reasons that in my view inhibit the growth of Data Science Strategy.

Some companies joined the bandwagon of data science because they wanted to be part of hype rather than be part of real value creation. These superficial goals often reflect that the top leadership is either not fully convinced about the effectiveness of data based strategy or was undertaking it due to one person’s fancy love (not the real knowledge) for the word “big data”. In the absence of clear mandate for data science as an input for business strategy, the organizational goals never got synced with the data science road-map and that led to unrealistic targets thereby resulting in abysmal failure.

The first and foremost thing to implement an effective enterprise strategy with regards to Data Science (irrespective whether your business model is B2B or B2C) is to embrace change. Often the rigid hierarchies, departmental silos, complex political organizational dynamics become a barrier in implementing a central data strategy that does not foster real innovation. Everyone wants to stake a claim of the portion of the pie or the complete pie itself without knowing whether they are the right person in the first place to stake that claim or without understanding the changes it would involve in the ecosystem in ownership of a portion of that pie.

A clear example: to implement nearly real time campaign response/next best offer model that is deployed in production, one might require synergy between different departments like Marketing, Sales, IT, Finance but that sometimes fails due to power play of different stakeholders and lack of willingness to embrace change which calls for harmonizing and unifying data and inputs from different sources and a closer synergy between different key stakeholders of those departments (who traditionally until that point were acting in silos for whatever political reasons).

Data Science could add more value to an organization if those hidden walls or barriers between groups are demolished and the data is unified. Politics often becomes a barrier. Or even if it is not possible to have a central data science structure, then a loosely held data science center of competence or excellence (resulting from a close partnership between 2 teams-For example IT and Marketing (or any other business function relevant to the data strategy) can be created that effectively engages to build a robust use case to solve a problem that becomes an example for other departments to follow the lead. If organisational dynamics do not allow even that, an Experimental Data Science Labs could be created which does not fall within the borders of specific Department/Team (but still has free access to the data source systems for experimentation, a parallel unit/team to existing Technology units/teams) and is free from the politically motivated agendas and is headed by an able, objective leader (more on that in the next point-the 'go to' person).

Everyone has a question, but whether it results in a useful business metric that can have a direct bearing on the business is often ignored.