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Archroma Management GmbH
Reaping the benefits of AI and ML
Analytics capabilities in terms of offerings, results, agility, and large data processing have grown many folds. In addition, the offerings are more intuitive, easy to learn with support from so many online training, 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 (BI) leaders whether self-service analytics is a boon or a curse for the organisation. Are we prepared to embrace fast-moving analytics and cater to the data needs of analytics?
In one of my previous experiences, we had the same dilemma. The traditional BI approach was always to have requirements written in stone and thrown it over the fence, then wait for something to come back.
Businesses did not have access to aggregated data, did not have options to play around with the KPIs and create what-if scenarios. As there were a lot of requirements and a centralised team, the backlog of requests was growing and thus leading to a 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 definitions of KPIs in different reports, and missing business ownership.
All leading to wrong information from your BI/Analytic, thus, creating a lack of trust in it.
Businesses could not stop, so there was a pocket of solutions that had started to emerge in business—where people started to acquire state-of-art solutions on the local level, which created a Tsunami of tools, reports, and KPI. There was no governance, and there was no way to control this Tsunami.
At that point of time, I came with an approach that these are two extreme ends, and neither of them Is sustainable as well as beneficial for business.
The scenario in the left is quite rigid and takes all flexibility and agility to market away from us, and right is too chaotic and unverified information.
Easier said than 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 a 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 a productive environment, thus making a single source of truth for data and KPIs
Conclusion
In our case, self-service analytics was a curse which we turned upside down to a boon. This was not easy as we need a 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 the global success of the organisation.
