Furthermore, a considerable number of the industries that struggled with the COVID-19 situation are network-reliant businesses: the banking, retail, automotive, hospitality industries all saw a dip in performance, leaving no room for wrong choices. But serving data to a large audience of dispersed employees who have different data literacy levels, different devices with different screen sizes, different hierarchical positions, and different metrics relevant to their job is a gargantuan task. Yet, some world-leading businesses have succeeded in filling up a network’s needs in data by adopting the practices of the new era of information.
This article will cover recent evolutions in the industry, as well as real use cases.
Building a Data Culture at Scale is hard.
In my previous article, “Sweating your Legacy BI tool”, I wrote about the eras of information and the creation of the era of communication focused on serving the needs of untrained business users. Every era of information was born out of a scalability issue. Modern BI came into the market because expert users needed independence from the IT, who couldn’t handle the strong demand anymore. Data Communication platforms are coming into the market because untrained users need an objective and simple clarity in the insights sent to them by expert users.
Modern BI markets itself as being simple to use, which is true when the message is targeted at an expert user who couldn’t handle the complexity of a legacy system. However, Modern BI still requires training and data literacy programs for the average employee, which causes delays in analytics adoption and therefore the creation of a data culture at scale.
Furthermore, Forrester reports an average of 2 to 5 BI tools per company, which adds further complexity to the mission due to the heterogeneity in deliverables, several environments to manage, and considerable financial costs. Team alignment with numerous tools operating at the same time is a considerable challenge, especially at the scale of a network. In my “BI Fabric” article, I spoke about the desire of BI tools to please all kinds of profiles, leading to undifferentiated offerings, and thus resulting in many overlapping capabilities, the same marketed promise, and the same results. Data communication tools have a clear understanding of their positioning within an analytical tool set, and know who they’re addressing. This is why moving into the era of communication will help companies gain focus and see considerable results.
Data Strategy 1 –
The Automotive Manufacturer: I need to communicate data to 6’000 untrained and dispersed users.
The network Performance Director of a leading automotive manufacturer needed to align 6’000 spare parts dealers located dispersed across the country. Spare parts have an expiration date, meaning they must be ordered based on current demand in order to reduce wasted products.
The data comes from the headquarters and needs to be distributed across the network by taking into consideration each of the 5 levels of end-user hierarchy, different mobile devices, and the fact that most users had received no training at all to read a dashboard.
The Performance Director wanted to encourage collaboration by setting up a platform that would enable anyone to understand the data communicated, and launch a data-driven conversation with neighboring peers in order to share best practices and improve performance.
The company had tried creating dashboards using a legacy BI tool, however, they ended not being used due to a low-quality user experience, and too much complexity for the untrained user. This halted the adoption to 15%, and teams had no feasible solution. Data had also already been worked on extensively and was ready to be exploited.
By moving to a data communication platform where technological choices were made in-product to handle large scale deployment of apps within large untrained audiences, the car manufacturer was able to leverage the work already done on the data and boost the adoption rates of analytics to 85% per week, with no end user training. This led to Xpts won in the customer’s market share 6 months after deployment.
Data strategy 2 –
The Food Service Provider: I need to communicate Predictive Analytics.
The world’s leading food service provider; which provides enterprise customers with cantines offering breakfast, lunch and dinner, needed to better manage their food items inventory to reduce waste. Inventory is ordered based on expected frequentation of the cantines in the next 90 days. The data is predicted in a predictive data algorithm based on 4 years of data. The data was ready to be shared, however, the data analysts had no easy way to distribute this information easily, across a network of 800 chiefs of site, on different mobile devices, and while respecting the user permissions of each hierarchy. Putting this data in the hands of so many individuals would help reduce the error margin of inventory supply from 11% to 7%, leading to yearly savings of €1,5M.
Data analysts were not communication specialists yet had invaluable data in their hands that they needed to communicate to thousands of untrained, dispersed, mobile-first users. The existing Modern BI platform did not offer the mobile-friendliness, end-user accessibility, and speed, and ease of deployment they were looking for.
It took 4 days for the team to broadcast predicted data in a storytelling format to chiefs of sites across the world, with immediate adoption, leading to €1,5M of savings each year.
data strategy 3 –
The Funeral Services Company: I don’t want to replace my Legacy BI tool. I’d like to leverage it.
A funeral services company had been operating on a Legacy BI system for years, however, their business users were not satisfied with it, and kept asking for a newer tool. The IT resisted the idea of completely replacing the platform because they had been training on the platform, and auditing, unplugging the existing system, then buying, and then training for a new one would require immense resources.
The IT had invested heavily in a legacy system, with actionable data ready to be shared with business users. However, the deliverables were not well-received by the business users who requested more ease and digestibility in their dashboards. The IT considered replacing the entire system with a Modern BI tool, until they understood they could simply give their legacy system a facelift by adding a layer of data communication on top of their existing software and provide user-friendly access to analytics.
The IT keeps their existing system and data assets, while business users are served data on a user-friendly platform designed for untrained audiences. €3M of auditing, unplugging, buying, retraining were saved, adoption rates tripled.
data strategy 4 –
The luxury retailer: I need to deploy data safely to my CFOs
The CFO of a leading luxury retailer wanted to be able to share financial information with the CFOs of each country the brand operated in, as well as with every store manager in order to provide performance updates. However, the data contained sensitive information and needed to be well limited in terms of hierarchy, country, and other conditions related to a person’s position within the company. The retailers used to set up the monthly report manually, 4 days per month, every month, to do so.
Serve a network of 30 CFOs and 200 store managers while ensuring strong data governance.
By moving onto a data communication platform made for large scale deployment, the financial team moved from 4 days per month to 2h per month in the creation of their reports, thanks to a built-in user experience, and no code setup of permissions and governance.
Deploying analytics is a difficult challenge, especially when required at the scale of a network. Successful companies have learned to adapt and leverage their existing data assets by moving into the new era of information, focused on communication.
Communicating information relies heavily on providing a clear and intuitive user experience; contrary to the complete freedom of dashboard design found in exploration tools, thereafter removing the subjectivity of dashboard design, and ensuring consistent clarity, accessibility, fast production times, and strong, scalable distribution governance for companies.