Aligning Business & Analytics to Drive Banking Success

Artificial Intelligence (AI) is disrupting industries across the board. In the banking world, analytics can sometimes clash with the opinions of decision-makers who use their intuitions and years of experience to make judgments and reach their goals. The most advanced data analytics system is pointless if the information it provides is not synchronized with the priorities of the business side of the bank. 

Business Intelligence vs. Data Analytics – A Tale of Disconnect  

In my previous job, I worked in the strategy department of a bank. My team and I worked with the head of the small business division to improve the use of the bank’s data – specifically, to use it to identify more sales opportunities and gain a deeper understanding of price sensitivities. We proceeded to explain to the analytics team exactly what we needed. 

What we were ultimately presented with was a sophisticated clustering model.  

When we presented the complex model to the manager of the small business division, he could not relate to it at all. 

Why did this happen? It’s because the analysts focused on pure numbers and data, while the business manager uses his intuition and past experience to shape his views on the market, the competition and the customer. The result of a very frustrating meeting was the inability to bridge the gap between the analysts, who were strictly data-focused, and the business manager, who could not see how the numbers were related to the reality he was seeing on the ground. 

This is a classic example of something I see happening over and over again – a miscommunication and lack of alignment between the analytical team and the business stakeholders.  

Business Intelligence vs. Data Analytics & Knowledge 

Who was right? What’s more important? 

When it comes to creating personalized products, is it more important to follow the numbers, or do the years of first-hand customer experience overrule what the data reveals?  

Analysts will say that data takes priority. They argue that advanced data analytics can accurately predict how customers will behave and what type of products and pricing they will best respond to. It’s based on this information that they believe that decisions should be made.  

The business managers, on the other hand, will take an opposing view. They claim that nothing is more important than meeting with and speaking to customers in order to hear firsthand what they want. This, combined with competitor research and keeping up with industry and market trends is what’s needed to make the best business decisions.   

The truth is that both are equally as important.  

The data doesn’t lie, especially when advanced AI algorithms are at play. This technology was built specifically to better predict customer demands and behaviors. But, this data does not exist in a vacuum and it can (and should) be enhanced by the knowledge that financial professionals gain by speaking to customers, observing competitors and having a strong feel for the market as a whole.  

How Earnix Bridges the Gap Between Business Intelligence vs. Analytics 

In order to understand how analytics and business can work together, let’s first break down how the analytical process works. When using analytics to price and personalize products, there is a process flow that includes: gathering data, building behavioral and financial models, creating pricing and personalization strategies, and then delivering prices and product offerings to the market (or to the bank to then deliver to the market).  

Too often, this process is treated as a whole – something that cannot be interrupted between the start and the finish. Like in my example above, the analysts went away and created their model from beginning to end – without receiving any input from the business side.  

The Winning Combination  

A better way is to break down the silos that so often exist in companies where multiple departments participate in a process but do not collaborate with each other. We can (and should) look at each step in the process as a discrete step, and understand that there are business stakeholders who may want to be involved at different points in the process. For example, a business manager who has his finger on the pulse of client behavior may be less interested in the data collection process but will have real value to add when it comes to adding input into the behavioral model.  

With a SaaS banking software solution like Earnix, each stakeholder in the company can access the part of the process that is most relevant to him/her. There is one source of data to ensure that everyone is working from the same baseline information from within one single platform that offers a separate working environment for the analytical side and for the business side. This means that there can be seamless handovers between analysts and business managers, leaving no excuse for non-collaboration.  

If the bank where I once worked was using Earnix at that time, the clustering model that the analytics team built would have been translated into the system and presented in a visual way. With all the relevant regulatory conditions already built into the model, the business manager would then be able to know and determine which segments will be relevant for him/her to use in order to achieve a specific objective / target.  

Instead of delivering pricing scenarios based on the ‘best guesses’ of the analysts, business managers can be presented with a variety of scenario outcomes, presented visually and before they are deployed – allowing the business manager to make educated judgements on which strategy should be implemented to achieve the best outcome in the current market conditions. 

By working together, the business stakeholders can identify the KPIs they want to track and the analysts can create “what if” scenarios so that the same model can be used to show what will happen in each scenario. This is a much more efficient process, saving time for the analysts and providing the decision makers with relevant and actionable data.  

Business Intelligence vs Data Analytics: Technology Collaboration Best Practices  

While we are lucky to live in an age where technology plays such a powerful role in business and AI can significantly enhance the bottom line at banks, let’s not forget the importance of the human component. It’s actually the combination of technology and the human experience that is truly where the power lies. Bearing that in mind, below are 4 tips to consider when the goal is to make analytics more accessible to the business  manager: 

  1. Infrastructure – start by ensuring that you have the right technical infrastructure set up to facilitate collaboration. The business managers and the data analysts do not need to sit together as long as they both are able to work from the same system and use the same data. 
  2. Human Interaction – a team must be built that includes both analysts and business stakeholders,  preferably facilitated by someone with experience in both sectors who can bring them together around a common goal.  
  3. Trust the Model – the business managers must be open to learning how data models work and understanding the value they can bring. 
  4. Share the Knowledge –  at the same time, the analysts must be willing to learn the context in which they need to build the models so that they can incorporate relevant market trends into the models. 

Over to You 

Bridging the gap and creating alignment between the data side and the business side will result in better work processes that can positively impact your bottom line – faster speed to market, fewer errors due to better collaboration, better governance, and overall more efficient operations. Earnix can help you facilitate collaboration between data analysts and relevant business stakeholders right from the start, ensuring that your business success stems from one single solution. 

Watch Noga’s full summit session right here.

 

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