The business journey to kleene reports and analysis

Fair warning: the next few sentences might feel like a sales pitch, but bear with me. There’s a method and a reason.

kleene.ai wants to make the journey to data nirvana as smooth, quick and efficient as possible. Our platform connects your data sources and makes data usable for great reporting and decision-making.

That all sounds great, especially if you’re responsible for delivering that data nirvana in your business. But we absolutely feel your pain on the internal sell of a tool to help with those steps.

Cool tech that does fancy stuff in the background needs to answer the inevitable question: “So what?”. What’s in this for the business? What’s the true benefit? What should the ultimate aims of your data strategy be?

We talked about the importance of data driven decision making in our earlier blog (link to DDDM blog). Let’s dive a little deeper.

Review your resource strategy

Humans are very good at some things. Computers are very good at others. It makes sense to deploy your resources accordingly.

Automate the repetitive. Assign humans to the cognitively complex, interesting and domain specific. Your team and your bottom line will benefit.

Construct a single source of truth, from which all reporting, analysis, insight and data science outputs are derived. Specialist human resources are now free to move to the value-adding end of the data pipeline.

In other words, automation is key to ensuring your smart data people can focus on looking forward. Here are a few applications they may like to get their teeth into:

Build a single customer view

Track each potential customer from first contact to an accurate lifetime value (LTV). Follow their progress as they browse across multiple touchpoints and make an initial purchase. Monitor costs of acquisition and marketing. Watch purchase behaviour, recency and frequency modelling. Then optimise your methods of customer engagement and communication.

Automate your P&L

Construct a general ledger and profit-and-loss model. Fully automated, with the maximum level of granularity across all dimensions. Schedule a daily report – without taking up time from your FP&A team. Allow your team to focus on forecasting, what-if scenario planning and hypothesis testing.

Empower true data science

Build data products to take your business to the next level. Leverage deep learning, neural networks, computer vision and the Internet of Things (IOT). Use deep maths and machine learning to attribute your marketing spend across channels. This is all made undoubtedly easier with a central data platform and single source of truth.

Deploy data engineering where it’s needed

Data engineering adds value to your business when it’s getting data right at its source. And when it leads the go-live of the data science products discussed above.

Without consistent, accurate and timely reporting, business analysis and insight are a struggle. Trust in data is often low due to inaccurate data or multiple answers to a single question. That leads to a chicken and egg scenario. Investment in data infrastructure is naturally perceived as risky and far removed from the realisation of value. But without investment, the valuable outcomes described above aren’t possible.

Need more ammo for your internal discussions? We’d love to help. Please get in touch.