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How to Create and Execute a Comprehensive Data Strategy
At the core of industry 4.0 is data.
According to the PWC South African Industry 4.0 report, 83% of respondents believe that data will be fundamental to any of their decisions. We cannot talk about artificial intelligence, data-driven strategies or setting up enterprise to take advantage of industry 4.0 without a clear data strategy. In addition, the volume of data and the increased complexity of data means that companies with a clear data strategy will be able to handle and exploit the data.
There are various considerations when developing a data strategy, chiefly among them should be the following components:
What business objectives is the data going to be used for?
A data strategy is not a standalone strategy. It exists to serve the broader objectives of the business. These objects could range from improving efficiency and understanding customer needs to create additional revenue streams.
Where will the data be collected from?
From understanding the business needs, the data that is required can be identified. The data strategy should define where the data can be sourced from. The strategy should note all the data that could be sourced internally and externally.
The strategy should also note the level of details that the data should have and how frequently it should be collected.
Where and how is the data going to be stored?
There are various technologies and tools that can be used to store or manipulate data. However, instead of focusing on the actual technology, the strategy should address challenges such as current data capability versus future data requirement, or how data will be consumed and stored.
The strategy can define whether a cloud solution or inhouse storage solution is required. Careful thought needs to be placed on this decision as going cloud is not always the best decision for every business.
The analytics that should be performed from the data
Some have argued that this is the most important part of a data strategy. It is the extraction of insights and the building of algorithms turns ensures that value is extracted from the data. There must be an alignment with what analytics are required to meet the business objectives, the availability of data and the business objectives.
Data governance and security
Given the increased value of data and increased threats of cyber security, it is important for the company’s data strategy to define security standards. This could be by means of tools, who should access the data, when should it be distributed and monitoring standards of the data.
Over the years, a number of roles (such as Chief Data Officer) have become prominent and have been tasked with being tasked with implementing the data strategy. However, to effectively implement a data strategy, a clear road map is required – one that is linked to the business’ KPIs (Key Performance Indicators).
More importantly, the culture of the business should be changed to embrace the value of data. Without data, industry 4.0 is simply a distant concept.