Adopting a data-driven strategy for your business
Adopting a data-driven strategy for your business.
By adopting “data-driven” strategies (or more sensibly in my opinion “data-enabled” strategies) companies can transform the way they do business, offering the kind of performance gains last seen when organisations went through transformations in their core processes 15 to 20 years ago. Such data-driven strategies depend on the following:-
a) The ability to harness data from multiple sources inside and, where appropriate, outside the business
b) The ability to build analytic models using this data to create insights, identify trends and predict outcomes
c) The willingness of managers to transform the business based on evidence gained. As Kaplan said “if you cannot measure it you cannot manage it”.
So the three key components of a data-driven strategy are Data (of course!); Analysis; Transformation.
The volumes of data generated by a business have grown enormously over recent years, and fortunately the tools and techniques have been developed to help businesses harness this data.
Much of the data that managers need to make decisions already exists in core systems but without the means to use it effectively this data has little business value. There are other, less-traditional sources of data that could have value to the business if only this could be tapped into. Social media data, weather data and so on. For too long decision-making has been constrained by a perception that there was insufficient data, sometimes only because, whilst the required data was actually available, it was deemed too difficult to access. Managers must now reverse this process by first deciding “what do I need to know in order to grow the business?” and then seeking out the data that you need to be able to answer the question.
It is essential therefore that a data-driven strategy should be supported by data management tools, processes and expertise to harness the data in a format that supports analysis, modelling and a new way of decision-making that is based on trusted information. This may involve data marts, data warehousing, in-memory data cubes and multiple other ways of formatting data for analysis, and this does require specialist help.
The ability to harness the data is important, but performance improvements and competitive advantage arise from what you do with it once it has been gathered together. It is when analytics models are applied to this data that managers are able to predict and optimize outcomes.
The range of analytics options is vast, typically residing within a Business Intelligence software suite and such is the maturity of this software today from the major vendors that pretty much all you need to do can be done. However, a word of caution is necessary at this point. The software will allow the most complex predictive models to be developed, but if they are so complex that they are not used, or even understood, then they offer no business benefit. The analytic process must be driven by the business, not the data scientists, and its aim should be performance improvement not complex, clever modelling.
Managers will only invest in business transformation if they have confidence in the data and the data models. Such confidence only comes if managers are involved and if the “experts” align the analytics to the culture of the business – after all, if these data models are not championed from the top they will be distrusted and any subsequent attempt to make changes will be met with resistance.
There could not be a better time to transform the way you do business. The tools and technologies are in place and there are companies, like Redland, with the skills and expertise to assist you in achieving your objectives.
For further information please simply contact our team for more information.