Trust issues with your Data and Analytics

Just 34% say they have a high level of confidence in their operational Data and Analytics (D&A), according to a recent KPMG study.


I can’t say I’m surprised about the huge trust gap. Having worked on many operating strategy and cost related projects, I have experienced the challenge of collecting reliable data over and over again. I have also designed and built several D&A solutions, and battled to ensure data quality, while reducing complexity and increasing transparency.
During my career, I have come to learn a few critical things that, when done right, help create D&A that business leaders will use and trust.

1. Find the right person for the job

Dealing with databases and Business Intelligence tools requires a different language and mindset. All too often, a lot of time and effort is wasted on building unnecessarily complex solutions and reports, just because business and IT people don’t understand each other. Poor communication in the design phase places trust at risk.
Building successful D&A, requires someone who can stand in the middle ground between business and IT, and define requirements in ways that allow for implementable and easily understandable technical solutions. I find that the best results are achieved when companies involve users with the right skills and analytical curiosity. Every organization has these persons – so make sure you pick the right one to lead and drive development of your D&A initiatives.

2. Invest time in training

Creating trust takes time, and developing D&A is no different. If people do not understand where data is extracted and how KPIs are calculated, they have a hard time trusting the figures. If you are fortunate enough to have included the users in design and development, you are halfway to success. Just don’t forget to spend time communicating and teaching the entire user base, in order to convince the other half. Also make sure that you make proper provision for training in your budget. Otherwise, the investment will fail to deliver the desired benefits.

3. Let analytics build trust in the data

D&A tools nowadays allow users to go beyond static dashboards and explore the underlying data. Enabling such analytics is a very powerful way to build trust. Many operational KPIs, such as utilization rate, cost efficiency and safety indicators, are ratios. Unless users can analyse the underlying components, it’s difficult for them to be sure of the data quality, let alone draw the right conclusions. I am an advocate of self-service analytics tools, as I have seen their ability to win over the trust of employees.

4. Ensure effective maintenance

Nothing breaks trust faster than inaccurate data. Seeing your production yield at an unrealistic level of 250% or a safety indicator showing 0 events, just after you read about an injury on the company intranet, will keep users from coming back.

Make sure you establish clear responsibilities for the data and clear lines of support when things go wrong or need modification. Too often, I have seen users switch back to old Excel reporting, just because their change requests have not been answered promptly. As business and operating models undergo rapid change, make sure that your D&A is not left behind. It takes a lot to win confidence, but little to lose it.

The same study that revealed the huge trust gap, also discovered that the majority of businesses see their competitive advantage as being underpinned by D&A. Regardless of whether your particular concern is operational efficiency, or understanding your customer, or managing risk and compliance, it is increasingly important to build D&A capabilities. Personally, I predict successful outcomes in all cases where sufficient resources and time, plus the right skills, are allowed to focus on delivering well-thought-out and tested solutions.

PS. Read the full report here.

Oskar Palva is a Senior Manager at KPMG Global Strategy Group. He has worked as an Advisor for Finnish and multinational companies for close to 10 years, mainly in industrial manufacturing and the chemicals sector. Oskar has experience in growth and innovation, operating strategy, performance improvement and data and analytics. Outside of the office, he enjoys a game of tennis, a good meal and the countryside.

Three Data and Analytics pitfalls and how to avoid them

Why too many analytics solutions don’t truly support business-critical decision-making and can sometimes limit trust in your organization?

Business intelligence solutions are increasingly becoming an everyday part of the corporate life, you probably recognize the impressively designed reporting dashboards created to give managers a view of a number of KPIs that enable fact-based decisions to be made with the help of nearly real time data. Unfortunately, the beautiful layout is too often partly window dressing, and problems start to arise when one looks deeper into the data presented.

After seeing how a number of business intelligence solutions play out in action, the majority of issues with them have stemmed from one or more of the three pitfalls that I strongly encourage everyone to avoid.


1. Take the quality of the data for granted

The first of the pitfalls sounds like it should not be a problem for any company making data-backed decisions – but unfortunately it is just too easily assumed to be in order. In short, the quality of data is often taken for granted. If your input data is inadequate and insufficient measures have been taken to secure its quality, the output will not be any better than the input. If your organization cannot rely on the data, it not only makes fact-based decision-making very demanding and time-consuming, but also decreases the valuable trust in your organization.

2. The more KPIs, the better

The second pitfall, is the assumption that “the more KPIs you have, the better decisions you make”. Focusing on too many KPIs can easily lead to a situation where one’s focus on things that really matter is put at risk. KPI, i.e. the Key Performance Indicator, so, out of the long list of potential Performance Indicators, you should have only a limited number of Key Performance Indicators that are selected specifically for your business and supports monitoring of the progress of the business strategic performance. Thorough assessment and evaluation after which selecting the Key performance indicators you avoid losing focus.

3. Use KPIs that don’t suit your business

Finally, probably the grossest pitfall is the lack of sufficient care in defining the calculation logic of the KPI, which most often results from a lack of true understanding of the business. If your business intelligence partner shines in the IT part, but takes shortcuts in understanding your business logic, you will most probably end up in a mess. KPIs lacking substance are worthless, and can even have severe consequences by prompting wrong decisions.

Trust your analytics and shine

Being able to trust in your analytics is one of the hot topics in the area of data and analytics. Why should you settle for being in the same position as 84 % of CEOs that have concerns about the quality of the data on which they base their decisions?

Presenting business reports in fancy dashboards for managers doesn’t make them trustworthy. Instead, trust stands for a larger set of KPIs and their relevance for your business. If you go wrong in your analytics, trust towards the whole organization can suffer. Don’t let it happen.

Niko Hollender is a project manager in KPMG’s Global Strategy Group committed to creating value for clients in the areas of strategy, operations and M&A. He has over six years of working experience in international management consulting. When off work, Niko enjoys going to the gym and training for his next marathon.