How to Launch a Data Analytics ProgramAdd bookmark
Introducing a data analytics project is an intimidating proposition. Faced with multiple vendor pitches, new must-have solutions, C-suite expectations, headlines, and promises of what can be achieved … how do you begin to move towards building your own Data Analytics secret weapon?
At Philips People Services Europe, Piotr Andryszak has just been through a lengthy implementation of his own – one that involved little drama, hardly any investment, but is showing powerful results. Having gone "live" with a new approach at the start of this year, he was pleased to share some of his experience, when SSON gave him a call. What's interesting about this case study is that it wasn't based on fancy technology solutions nor was a top-shelf data team parachuted in. Instead, a lot of effort went into behavioral transformation that focused on connecting employee inputs with operational outputs. The only way to introduce value-adding data analytics is by integrating it into behavioral transformation, through operational excellence. The real change, Piotr explains, took place at the organization DNA level. Here is an extract of an interview conducted by SSON’s Barbara Hodge.
Interview with Piotr Andryszak, Manager, Philips People Services Center Europe
Q: What was the original burning platform that drove you to adopt data analytics?
Initially there was no burning platform at all, or rather we were not accepting that there was one. At a certain point we realized we needed to move to the next level of our operations. So we made it clear that our operations needed to deliver according to our commitments (reflected via green KPI dashboard).
So, how and where to start? We concluded that although we were all monitoring specific KPIs, reporting, declaring root causes, taking corrective actions … a lot of that was guesswork. We weren't 100% certain about the impact of actions on output figures, and there was a lot of uncertainty around driving decisions on the basis of our data.
This wasn't good enough. If you don't completely trust your data and if you cannot interpret it, you cannot take actions on the basis of it. So we decided to be more honest in evaluating the quality of our data analysis and look at it differently.
The data and its treatment was not the core aspect of the approach, however. It was about a change in our behaviors and the way we envision our organization and individual roles in within Philips. We made an effort to understand the impact of what we do and how it relates to business. In that sense we had to introduce so called strategic alignment – relating daily activity to business results, and how those contributed to the whole of Philips. The tangible part of this approach was the introduction of more frequent monitoring of critical elements and correlating those factors with what had happened on that particular day or over that particular period. We wanted to understand the details and what drove those numbers. Moreover we wanted to be very specific and transparent on an individual level. Up until then, most of the staff didn't feel that KPIs related to their daily work. Obviously that was an unsatisfactory scenario and it became our main focus for change.
Once our team understood the correlation between their inputs and business outputs, they also knew what they needed to do to improve performance by proper focus and prioritization and addressing issues immediately when they appeared.
Q: What kind of technologies did you use to leverage data analytics?
PA: We didn't use sophisticated data analytics tools. Instead, we used the tools that we were already in use – standard reporting and analytics tools likeCMS, our case management system (SM9); telephony (InIn); and Net Promoter Score survey (Vovici) – all of which had an analytics element to them, albeit not a very user-friendly or robust one. What we realized is that we needed to create more user-friendly formats for the analytics and we did so by exporting this data into simple Excel spreadsheets that more clearly identified what we were looking for.
Q: How did you communicate the ongoing impact of data analytics to stakeholders and customers?
We recognized that visualization is an important element of buying into the analytics. One of the early things we did was create a competition around a logo and design branding for this journey, which we called "the Road to Green". We used that branding at every stage, and tagged all our activities with it.
A couple of change management aspects we employed included producing eye-catching visuals and graphics that were hung on the wall and accessible virtually. The idea was to visually communicate our performance on the path to green KPIs. To keep the roadmap at the front of our minds, we posted a big image of it on the wall and tracked progress by moving country flags along its path. As simple as this is, it made an enormous impact on our staff as they visually recognized the correlation between their quality and timeliness of input, and the quality and timeliness of business units’ performance. To make it even more transparent and visual we used color-coding on a daily basis. Daily reporting circulated to all our employees clearly defined expectations from each and every employee.
The symbolic value of measuring our progress along the "journey map" has been a very important factor. As we reach different milestones, we have been and are recognizing our achievements and celebrating.
On a side note, we also recognized that our teams align more easily with countries than with process and so we drilled down into the data to country level, tracking our impact on a country-by-country basis. So in that sense we have adopted our earlier approach. This has been highly effective.
When talking about other stakeholders, we have also made significant progress. Firstly, by recognizing those that are impactful and how we could influence that audience. Then we have also taken an active part in starting to communicate what we do on our Road to Green and how that positively impacts the interest areas of our stakeholders. And we listened to suggestions including reading in-between-lines information and feelings. We knew that it was all about perception.
As a result, and in no time, we changed our perception from problem makers to the ones leading the change.
Q: What is the potential impact of data analytics for HR?
There are a couple of key benefits that a data analytics focus enables. First, the most important thing is transparencyfor all stakeholders (including our own employees). This provides a clear, fact-based picture of where the organization is. It also enables prioritization: using the data to drill down to particular issues, and size up the potential impact of actions, the benefits of change, etc. It supports change and business transformation as it makes and supports the case for achieving desired results through the new model. It identifies opportunities and savings, for example in pure funds recovery (i.e. social contribution overpayment), or other in scope of opportunities in HR operations – gross to net calculation, national insurance, labor laws application, work insurance, social withholding tax, local and European grants, etc. Finally, it also minimizes risk as a result of monitoring statutory reporting, contribution payments, alignment to policies, avoidance of fraud, etc.
Q: How has data analytics supported Philips’ business?
This project has helped to drive transparency and understanding of the impacts of our operations. It creates a link between what we do, how we perform and the impact on our clients. In that sense, it is the first step towards delivering services as per our promise and according to our customers’ expectations. That in turn is the first step towards creating value that matters to our customers. By transforming behaviors within the organization and moving towards a performance culture, we can partner with the business to understand its needs and priorities. Analytics enable us to cash in on opportunities.
Q: When did you launch this initiative and how long did it take to get going?
We started in May 2014 and had a fairly lengthy change management transition that ended in January of this year . We've now been operating under the new way for just about 10 months. The high performance is now well integrated into the organization as demonstrated by our stable KPIs and steadily growing trends.
Q: How do you measure the ROI of your investment in data analytics?
The returns are measured in process optimization and improvement, for example, through the value of corrected anomalies, of non-value-add activities discontinued, and the benefits derived by re-engaging freed-up resources.
There are also future financial benefits as a result of optimizing employer contributions by considering benefits vs. costs. Finally, we could also measure returns through risk avoidance of potential legislative and contractual penalties.
Q: Can you share some of the improvements that you've been able to demonstrate as a result of tracking these analytics?
The power of an employee recognizing the connection between following certain rules and seeing progress first hand simply cannot be overstated. I saw how incredibly empowering it was for an individual to recognize that their numbers were helping the performance that we were tracking visually. Here are some examples of our performance improvements:
- Case Fulfillment Rate – improved by 23 percentage points in the period
- First Call Resolution Rate – improved by 38 percentage points in the period
- Abandon Call Rate - improved by 5 percentage points in the period
- Net Promoter Score - improved by 10 percentage points in the period
- All above reached and kept above targets
Q: What are some tips you would share with others who want to start their own data analytics program?
The data is really already there. The opportunity is easily overlooked, however, or at least has been in the past. The most important thing we did was we dedicated effort to changing people’s perception, behaviors and the organization, by which I mean changing the culture. The culture drives performance and our performance drives the culture. Today, understanding analytics is in our DNA. Operational excellence is understood to be dependent on a strategy that includes data mining, analysis, and improvement.
Q: To what extent has branding played a role?
Branding has been one of our key success factors. We made great use of our branding and tag various activities so that they were easily recognized. We used to call this initiative the "Road to Green" – meaning, we wanted to drive greener dashboards and KPIs as far as our performance is concerned. Making everything transparent, progress visible, and keeping the focus and engagement visualizations and explicit branding were of key importance.
Q: Have you had to make big investments?
Not really as far as the technology or implementation is concerned. We stuck to our current KPIs and the tools that we already had been using. What we did, however, was improve the reporting by converting it into a more readable format that could be easily understood by everyone. It was important that we could clearly understand what drove particular daily behaviors so that we could identify and influence these drivers. For example, abandoned calls were already being tracked in our telephony system – we had the data sets within the tool – but because it wasn't user-friendly it wasn't easy to mine, let alone understand and analyze.
None of this is big magic. The main change is showing employees the connection between what they do in their day-to-day work and the output i.e., how we impact business unit performance. Once they recognized that, they were able to use the data analytics that we were now more regularly tracking to measure their progress along the road map. Everyone is engaged and everyone cares. The results speak for themselves.
The most critical investment was made into people development and capability building. Starting from the leadership, from myself and our core leadership, focusing on different aspects of strategic leadership, building successful teams, and growing the organization.
Q: How is the shift from ‘reporting’ to ‘analytics’ supporting enterprise/ operational performance?
It is about questioning unnecessary reports, starting to design reporting that fits the purpose, and using analytics to deep dive and understand the nature of a problem. Analytics is about explaining and finding root cause, not just presenting the data through reports. That is actually representing one of our key priorities for future.
Q: How are you upskilling/supporting your employees so they can take on these new analytics strategies?
Our next aspiration (BTW co-created with all our employees, already in progress) is to transform our organization from Green to Great.