In my previous post, I discussed how important data management is for successful advanced analytics based on a recent TDWI report I found of interest. As a follow on, this month, I examine what organisations can do to overcome some of the main barriers to tailoring data management for advanced analytics, if you really want to reap the benefits.
Constant governance pressure
According to a TDWI report, the leading concern remains data governance. More specifically, how best to modernise governance practices to cover the inevitable additional data platforms, and use cases, which come from advanced analytics programmes.
Data legislation is something that needs to be taken seriously. The General Data Protection Regulation (GDPR) in the EU and the Protection of Personal Information Act (PoPI) in South Africa are prime examples of this. The financial and reputational damage that can result from non-compliance can be significant and so it’s no surprise that data governance remains a top priority to get right before any benefits can be reaped.
Irrespective of the legislation, ensuring data privacy and using it within compliance parameters will underpin any successful analytics initiative. Of course, given the right focus and resources, an organisation can maintain its compliance no matter how many data platforms and advanced analytics programmes it runs.
Managing data complexities
Another barrier which TDWI highlights is the complexity of hybrid data architectures. It states that most data management teams deploy multiple data platforms, each optimised for a particular [analytics] use case or structure. Inevitably, this creates a distributed environment that can result in significant data sprawl.
This complexity is vital to provide the best outcome for specific use cases. However, it creates difficulties in architecting environments that can continuously repurpose data for a variety of uses or purposes. Maintaining a single version of the truth can be problematic given how data is spread across environments within any business. If data is not successfully integrated across these architectures, a business can lose sight of the most relevant aspects of it. The saying of not seeing the forest for the trees becomes all too real in this regard.
Leveraging expertise
Furthermore, TDWI has found that companies that are new to advanced analytics are often held back, at least initially, because of a lack of internal skills. It goes so far to say that even those companies with established analytics programmes struggle to keep pace with the skills required to unlock the potential of the data.
And the skills gap that exists will certainly add to organisational pressure. More must be done to upskill and reskill data management teams in this data driven world. Moreover, the training provided to tertiary students must better reflect the needs of the organisation especially when it comes to advanced analytics.
Fortunately, none of these obstacles are insurmountable.
However, a concerted effort must be made if companies are to address these concerns and positively impact change at the organisation, to be able to reap the benefits data management for advanced analytics offers. Join me next month for the final article on this topic, where I explore the critical data management capabilities required to ensure advanced analytics success despite the challenging market conditions.
