Naveen Gupta, Global Head of Data Governance & Analytics, Archroma Management GmbH
Analytics capabilities in terms of offerings, results, agility and large data processing has grown many folds. In addition, the offerings are more intuitive, easy to learn with support from so many online trainings, videos and how to guides.
This also expanded the horizon to how easily people can switch from excel pivot tables to more analytical, real time and user designed solutions thus enabling Self Service Analytics.
This had posed a big question on Data Analytics or traditionally Business Intelligence leaders weather Self Service Analytics Is a Boon or a Curse for the organization. Are we prepared to embrace fast moving analytics and cater to data needs of analytics?
In one of my previous experience, we had the same dilemma. Traditional BI approach was always to have requirements written in stone and thrown over the fence and wait for something to come back.
Business did not have access to aggregated data, did not have options to play around with the KPI’s and create what if scenario. As there was lot of requirement and a centralized team, backlog of request was growing and thus leading to long delivery time of the requests. This also impacted the quality as there was pressure to deliver fast which resulted in overlooking some of the aspects such as performance, duplication of KPI in different reports/ analysis, different definition of KPI’s in different reports, missing business ownership. In all leading to wrong information from your BI/Analytic thus creating lack of trust in it.
Business cannot stop so there was pocket of solutions started to emerge in business where people started to acquire state of art solutions on local level and that created a Tsunami of tools, reports and KPI. There was no governance and there was no way to control the Tsunami.
I would depict the two situations as below:
At that point of time, I came with an approach that these are two extremes end and neither of them are sustainable as well as beneficial for business.
Left is quite rigid and takes all flexibility and agility to market away from us and right is too chaotic and unverified information.
Based on that, I started self-service analytics which was somewhere In between as below:
Easier said then done. What we stated for this approach was to identify people in local business units who were on the far right and enabling and empowering them with data and tools so that they can create their analysis.
These solutions would be then verified centrally and moved to a productive space for broader audience to use. This helped with covering topics such as:
• Trust in data and reports as these were originated within the business
• Collaboration to verify and check the results of analytics more carefully
• Agility as we had more people to provide Inputs, prepare the analysis and even verify them
• Globally verified Information goes into productive environment thus making single source of truth for data and KPI’s
In our case, self service analytics was a curse which we turned upside down to a boon. This was not easy as need lot of collaboration, support and energy to make this happen.
My advice to Data and Analytics leader would be to look for synergies with local business units on what they are doing and make them your allies. Enable and empower them so they can contribute to not only local success but also to global success of the orgnization.