Much attention has been focused on the subject of data science in the last couple of years. There’s been plenty of activity in the field: universities offering data analytics courses, emergence of startups devoted to business analytics, and corporations setting up dedicated departments to read patterns in data. A great deal is expected from this field in the next decade.

Now, in my opinion, data organisation is the first step in getting to grips with data science. I will focus attention on agribusiness in this article.

Agri-business is one of the oldest industries known to man and has evolved considerably over the centuries… new cropping techniques, selective livestock breeding, creation of hedging tools, establishment of property rights in land, improvement in storage & logistics etc to list just a few.

Today, we are on the cusp of an information revolution where digitisation of these previous revolutions will create a multiplier effect in agri-business. The players who adopt the new technology early will benefit the most from such a revolution.

What exactly does this mean? The usual questions asked about data science are: Will it predict corn prices in 2020? Can it help in predicting port closures? Can it predict how the next risk event will manifest itself? Is data science some kind of technology reporting? The answer is… it depends.

Data science in agri-business is not a crystal ball; rather, it helps one to take the right decisions with the information at hand. So it comes back to one key element, which is the collection, organisation, and storage of data. Having the all-important data that includes transactions, behaviours, numbers, and texts is good – but it is not enough. The most important thing is the proper organisation of these different classes of data.

The first step a company needs to take is to invest in data organisation, which is distinct from data storage. Different businesses tend to store data in different formats based on their expected uses of the information.

In addition, IT (information technology) departments seldom understand how each data source would be typically used. As such, they might store the data across business units in a large data storage unit.

Some firms take the additional step of collating the data and transforming it into a common format across different functions, but it still falls short of the requirements of rigorous analysis. Agri-business firms in general underutilise data and don’t place much emphasis on its organisation.

How does data organisation help? First, it reduces costs for data cleaning. Second, it helps in a seamless transfer of information and analytics across the firm. Third, it helps in faster, cheaper, and more effective analytics. Fourth, it helps in deciding the right environment for cloud architecture.

Data organisation is a first step towards monetising data as an asset. As we move into a greater machine-to-machine world, strategic organisation of data will enable firms to both leverage insights from cross BU/function analytics and achieve greater return on investment from their information systems and storage.

The writer is based in London and is the founder and Managing Director of OpalCrest. Views are personal.

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