Meeting the challenge?

Today, more than ever, organisations are seeking to harness their information assets as effectively as they can.  Many are introducing automation to improve efficiencies, cloud computing to reduce costs and AI/machine learning to deliver a further range of benefits.  Often the transformation needed by mature organisations with complex IT architectures requires a commitment to complex, multi-year change programmes.

Not addressing the constraints of aging, legacy systems leaves companies vulnerable to new, disruptive entrants that are not hampered by the baggage of the past.  Hence, the strategic, enterprise-wide initiatives being undertaken by organisations of all hues to modernise and become “data driven”.

The creation of executive roles such as Chief Data Officers (CDOs) and Chief Data and Analytics Officers (CDAOs) alongside the wide-spread adoption of data governance initiatives marks the growing importance of controlling data to support corporate strategy.  A focus has been on improving the quality and value of data assets.  Rightly so, as they are the foundation of information that flows and is used throughout organisations.  But what is happening downstream of these on-prem or cloud data stores, lakes or lake houses?...

What control is being applied to Analytics assets?  How are responsible C-suite executives equipped to control and curate the applications that source governed data and produce much of the information used for decision-making?

Most companies that have undertaken some form of digital transformation will continue to use multiple, disparate BI and analytics applications.  This presents a range of challenges that left unaddressed will result in continued issues and the potential benefits from improvements to data assets not being fully realised.  Some of the challenges I’m aware of by team are:

For analysts, developers and support teams:

  • How can we identify inconsistencies and address proactively?
  • How can we analyse issues, react and resolve quickly with reduced effort?
  • How can we simplify and optimise the portfolio of BI & analytics applications?
  • How can we reduce repetitive manual tasks and focus on analysis?

For management:

  • How can we mitigate the risk of mis-reporting?
  • How can we optimise our BI & Analytics resources?
  • How can we deliver change programmes efficiently and with reduced risk?
  • How can we maximise the value of our data and analytics assets?

For consumers of analytic output:

  • What degree of confidence to attach to metrics?
  • Where to find the appropriate information across so many apps?
  • How is information produced and in what context should it be used?

What challenges do you experience in producing or using BI and analytics?  How do you tackle them now and what, if anything, are you hoping for by way of improvement?

Green shoots are emerging in the form of governance practices and new software solutions to help address some of the above.  Metonomy, for one, enables organisations to increase the value of their analytics assets through metadata automation software.  It is now possible, using such solutions, to extend enhanced governance and control to the “last mile” of an enterprise information flow.

We will be sharing more insights and guidance in future posts, so if you are wanting to find out more about governance and how to improve trust in your data, then stay tuned.  Find out more about the art of the possible, today, at: