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By Irina Steenbeek, Founder of 'Data Crossroads'
In our previous blog, we talked about the importance of data in your daily activities. If data is an asset it should be treated and managed accordingly. So, what is data management? And what does it mean for you as a business planning professional?
Data management is a business function that, in my opinion, every company should have. Still, often it is not the case. Managing data means that everybody in a company knows what data they (can) get and have a good understanding of what it can be used for. It also means receiving data when and where it is needed. And finally, producing and working with data of acceptable quality.
If you want to be fully satisfied with the data you get, you need to ask yourself the following 5 questions:
It is still a common practice for finance departments to produce a large number of reports. The question remains: do the recipients of these reports recipients read, understand and use the information they provide? You should periodically check the information needs of your stakeholders. The data that you get as an input must meet these information requirements. The supply of information needs to correspond with its demand. Aligning the data flow according to ‘supply and demand’ will optimize a lot of resources used in this process.
Data has meaning only if you put it in context. For example, ‘100’ as a number has no particular meaning. But it you add € or km/h to it, it stops being just a number and becomes information. The same rule applies to data you receive or deliver. You always need to be sure about its content. If your business partner talks about sales and you mention revenue, are you still talking about the same thing?
One of the best ways to speak the same language is developing a company business glossary. This is a project that could be initiated by the data management team of your company. For you, the easiest way would be simply enhancing your reports with some basic definitions of the main terms.
A couple of days ago, at a vendor presentation, I heard some interesting statistics: for a business analyst, it costs on average 4-6 weeks to find the location of certain data in a database. Very often the finance department gets requirements for the provision of new information. These requirements mostly arise either from new regulations or updated demands of the decision-makers. The main challenge is to find out whether such data exists and if so, where it is located. Yes, the IT department is responsible for the execution of the job. Your accountability lies in the following two areas:
A lot of companies have a huge number of IT applications where data is hidden. If you put on paper the relationships between all of these applications, the picture will remind you of a plate of spaghetti. Your management, audit or regulatory bodies very often require an explanation as for the origin of the figures in your reports. To give a clear explanation, you need to know the origin of data and what has happened to it on its way to you. It might cost a lot of your valuable time to find reasonable explanations for the figures. Furthermore, if you find a repetitive error in data, you will need to have it fixed. To make the task easier, you need to document the path that certain data has traveled from its origin to the end destination. It is the IT or the data management department that will have to do this for you. You should be aware of this challenge and be clear about your requirements. Such documentation consumes a lot of time and resources. Finance, as the most important stakeholder, can become the sponsor of this initiative.
Data is a company asset. All major departments have concerns about data circulating within the company. So, also data quality is a shared responsibility. Still, finance is the one department that suffers most from poor data quality.
Very often, finance is taking responsibility for cleansing the data, which is in fact, quite unfair. So, how do you resolve this situation? First, clearly define and communicate your requirements for the quality of data you receive. Secondly, take leadership in data quality initiatives. In one of the following blogs, we will come back to the subject of data quality and examine it in closer detail.
You are now equipped with the set of question about data you should ask yourself and your colleagues tomorrow. Once you have the answers, you will know which information is required and you will be sure about the meaning of the data you get. Also, you will become well informed about the origin of your data and the transformations it undergoes on the way to your table.
I referred to the necessity to specify your data requirements several times now. But what should your requirements be, and what is an efficient way to specify them? Stay tuned: in the next blog I will give you some practical tips on how to do it.
The article was first published in Unit 4 Prevero Blog
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