Data Strategy

The term “Strategy” refers to one of the main streams in management theories and organizational practices. Henry Mintzberg from McGill University defines strategy as a pattern in a stream of decisions in organizations.

In data eco-system when we talk about data strategy, we apply it in order to set a high level view of valuable outcomes in alignment with business strategies. In a broad meaning, strategy has some corresponding elements including people, processes and infrastructures.

The Challenge

One of the main challenges in organizations dealing with data and analytics, comes from different perspectives about the strategy of the business and the appropriate data strategy underneath it. In many organizations we observe that there is no balance with technical teams and managerial expectations of data strategy.

Technical teams which in many cases are very sophisticated in their abilities and technical skills, deal with the challenges of data quality, data integration, storage management, administration challenges and so on, thus they don’t entirely understand the business requirements. This contradiction in views between data operating layer of the company and managerial level, is one of the most critical challenges in gaining insights about customers and competitors.

Proposed Solution

There are so many articles and whitepapers trying to outline many steps for organizational departments and prescribing some responsibilities for CIOs or CDOs for filling the mentioned gap. Based on my experience, I propose 3 main goals which by ensuring about them, the managerial level can get the most benefit from them:

1.   Data Democratization:

2.   “Add people” to get insights of data

3.   “Eliminate people” in data preparation and data quality

Data democratization

It means “Making sure good data is available to everyone, on demand, with little or no delays”. There are so many practices in data management tasks which results in data democratization including data governance, meta-data management and so on.

If every employee of the company, regardless of the level of accountability, have the ability to get the enough amount of data and reports, we can say that the first step has been done.

 “Add people” to get insights of data

Many top level managers believe that by implementing the new cutting-edge technologies in data visualization and data analytics, they can get the insight-full decisions. I can say that this is completely wrong. Making efficient decisions has nothing to do with tools and the mature level of analytics, it is all about the ability of the managerial team, especially CEO to gain actionable knowledge about the important topics such as customer behaviors, competitor’s actions, market penetration and combination, new trends in related technologies and etc.

Adding data analytics professionals to the highest level of companies seems crucial to CEOs. As we all know, the “C-Level” managers including CIOs & CDOs have some routine meetings and cooperate in decision making process but in action we have seen that the CEO’s main decisions are not originated inside these sessions and they just bring their own insights inside the sessions.

The much interaction between the “business and data analysts (such as CIO)” and “CEO”, the better outcomes for companies.

 “Eliminate people” in data preparation and data quality

Nowadays the data preparation processes in technical departments, is getting tougher and tougher. Ensuring that the data is correct and we can rely on them can have direct impact on decisions and affects the overall effectiveness of the company. In order to deliver a truth-full data, some parts of ETL process has been done by human interactions.

Automating the data preparation processes is another crucial step in order to increase the trustworthy level of data inside the company.

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