Recently, we have been working on some very interesting – and challenging – data projects. Underlying many of these projects has been the customer organisation's desire to utilise AI in their sales and marketing operations, in business development, or reporting and analytics.
Mapping out AI opportunities is heavily dependent on data, so we start by getting to know our customers’ data systems and how they collect and use that data.
As part of this mapping process, we have initiated many fascinating conversations, witnessed numerous moments of discovery and cleared up a number of misconceptions.
I'd like to share a few examples to really drive home why it’s worth talking about your data needs and goals out loud, often, and with a wider team.
Example 1:
With an established SME, we discovered that they had as many as eight separate databases in daily use, with zero integrations. Critical customer information was scattered across the different systems and a lot of data was manually duplicated from one place to another. Few people knew what data was up-to-date and where to even look for it.
There was no master data system and dozens of additional and supporting systems had been purchased to fill this gap, without a clear development plan. We helped identify the need to upgrade specific data systems and prepared a phased development plan.
Data management maturity level: poor. AI readiness: non-existent.
Example 2:
In an organisation that was highly committed to improving customer experience, customer data was shared interest for many teams and data was being actively collected. They needed our help with leveraging. Data analytics was considered to be in great shape, but they were disappointment when the data views failed to spark new development ideas.
We heard that the organisation had at best 1 200 different Power BI reports. 80% of these were deleted without anyone ever asking after them.
We concluded that the organisation's main problem was that there was no structure for using data for business development. None of the teams had designed an operational model for analysing data and generating improvement ideas. Together we designed an approach where the responsibility for interpreting data and making data-driven decisions is shared between different teams and integrated into day-to-day activities.
Data management maturity level: satisfactory. AI readiness: satisfactory. Maturity level of data-based decision-making: poor.
Example 3:
While mapping the AI capabilities of another of our customers, we brought together sales, marketing, development and IT to discuss the challenges and goals. During the discussion, the sales manager said: "We don't bother giving quotes for all the leads because the tool is so slow and cumbersome..." This information came as a surprise to the head of development, who commented: "is that so? Well, we'll need to address that right away."
When the conversation turned to analytics, the marketing representative said that her team uses three separate analytics systems but none of these show how the marketing efforts impact sales. The IT representative was able to explain that such a view exists, but it has been built in yet another system.
Data management maturity level: satisfactory. AI readiness: moderate.
Examples 4 and 5:
A large food producer wanted to monitor its marketing performance using an intelligent dashboard for could leverage AI. The challenge was that the operator's advertising and media agency partners owned all the marketing campaign data. Results were only monitored through separate, non-integrated PDF reports.
To enable reliable, transparent MMM and ROMI tracking, we first needed to gather all marketing performance data into the organisation’s own databases.
A very similar challenge was encountered with a beverage industry player. The company was spending millions of euros a year on marketing, but nowhere could they see what impact this was having on their business.
We combined the necessary data sources and built a centralised data view in Power BI to enable sales funnel monitoring, total sales forecasting, more precise targeting, and performance improvements.
Data management maturity level: poor x 2
AI readiness: poor x 2
As the above examples show, even if the importance of collecting and storing data has already been understood, data management practices may be lacking. In such situations, the use of data remains fragmented and sporadic at best, leaving no real opportunity to apply AI to enrich and analyse customer data.
AI capabilities need to be built on top of reliable, high-quality data management. This often requires the development of a coherent data ecosystem. Sometimes integrating the data systems and centralising data into a single database requires just a few easy fixes. It might not take even half a year to get a specific part of the data in such an order that intelligent analytics and AI can be tested. However, if essential data systems need to be replaced, the timeline will stretch out. The key thing to remember is that your data ecosystem development will always be an ongoing project.
I would like to underline that utilising your data is more than playing around with AI. People are still needed to interpret analytics, to formulate observations and make actual development decisions. AI can surface insights, but it cannot independently decide how to develop your business or services. At least not for a while yet. Not in most organisations anyway.
The structures and practices for managing and using data, and for data-based decision-making – whether AI-assisted or not – need to be integrated into the day-to-day operations of an organisation. I suppose such practices can in theory evolve organically in a very proactive and flat organisation, but it is clearer and faster to get on track by implementing a development project with the help of a trusted and experienced partner.
If you need help unravelling your data, we are ready to help. Contact us!