In the past few months I have had discussions with several firms who wanted to capitalise on their data assets. Their starting point was to recruit someone who knew what they were doing. Great idea, but in practice many of these firms were looking for the wrong person. Shocking to see that they were looking for a Data Scientist or BI manager who was predominantly a developer. Bad idea. In at least two cases they had hired someone and when that person hadn't worked out they hired someone to replace; cast in the same mould. Double bad idea.
The fundamental issue here is that they started in the wrong place. In order to succeed the lead for Analytics, Data or Business Intelligence in organisations needs to have a broad set of skills to succeed. Let me explain by illustrating with the reasons these organisations have failed.
"If you don't know how to get things done in an organisation the outcome will be poor".
The data lead needs to start with a good understanding of the business and the stakeholders and the culture. At the end of the day these are the things that underpin the success of any initiative. They need to be able to navigate the organisation and quickly understand how to deliver success. Any program Director would tell you that.
"If you don't fix the data at source then you end up with garbage in and garbage out".
Sorting out data governance is critical. Inevitably data needs to be fixed at source otherwise there will be many workarounds and you build up technical debt which eventually becomes too complex and rapidly things decline. What needs to be done is fixing the data at source which involves getting the business to take ownership of their data quality, create common definitions, use master data management to enable you to join the data sets and also fix the business processes to ensure data is captured accurately at the right time.
"If you don't build your data stores fast enough you will make things worse".
This issue has been highlighted by recent big data discussions. If it takes you more then two years to build your data repositories (and in this I mean predominantly your warehouse) then it will never get built. Other parts of the business will lose patience and build there own, exacerbating the issue. Also, the business will change and the data structures and technical debt prove too much of a burden to succeed. So here the argument is use automation tools, and less hand crafting of ETL and transformations.
"If you don't redesign the business processes to include insight then they won't use it".
Finally, let's assume you have some wonderful data that is transparent and accessible and you have data scientists who are happy because they can add value. (Let's face it if the tasks above had not been done your data scientists will have left the firm, frustrated, by now, which happens a lot). The final stage is to embed the insight into the business which means ensuring that it supports people in their roles. In practice this means it's embedded into operational activities such as pricing, and useful visualisations are created to enable the business to make decisions.
If you follow my thought process you'll realise that in order to succeed the person you need isn't a technical BI manager, or a Data Scientist. It's actually a seasoned BI Director or Chief Data Officer who understands the fundamentals of data, process, business, people management, knows how to navigate around an organisation, and has a solid understanding of the appropriate technology and analytics.
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