Want to Build a True Data-Centric Enterprise? Embrace Your Customer

Alex Golbin, Chief Data Officer, Morningstar on why customer-centricity is key to data and analytics success

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If you like what you read here, don’t forget to register to attend the Cloud Strategy for Actioning Data virtual event taking place October 26-27 where Alex Goblin will be presenting on “Actioning Timely Data To Outpace Disruption & Spotlight Differentiated Value.”

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For decades, many organizations have primarily used data as a means for measuring and optimizing operational efficiency. In other words, it was used to cut costs. In fact, according to our own research, 43% of data leaders say that the #1 way data analytics delivers value to their organization is by increasing efficiency.

However, for those looking to develop world-class data analytics capabilities, traditional process-focused techniques will only take you so far. The time has come to embrace customer-centric analytics.

Customer-centric data and analytics don’t just cut waste, they add value. As Alex Golbin, Chief Data Officer, Morningstar explains, “A truly data centric organization is a truly client centric organization. At least in my experience working in financial services, a client's interests always comes first, second, third, fourth, and all the way up the chain. I like to say that client experience rhymes with data quality.”

He also adds, “Based on what I've seen, you can be data-centric, but that doesn't mean you're going to be client-centric. However, if you're truly client-centric truly, and you can execute on being client-centric, you're going to be data-centric, no matter what.” 

In other words, customer-centricity leads to true data-centricity. But, you may ask, how does one go about building a customer-centric data strategy? 

 

Data Governance

Above everything else, trust is the most important aspect of any data strategy. Alex tells us, “I've been on a number of client conversations throughout my career where the conversation comes to a stop the moment clients spot an incorrect data point, because then the question becomes, if your data is wrong, how can I possibly trust the recommendation from your model? It could be the most sophisticated model in the world, but at the end of the day, how can I trust my money, my retirement, my livelihood, to the output of the model, where I can't really get comfortable with the data coming in. So by necessity, if you want to build that trust level with your client, you need to bring that trust level with the data. So hence organizations do become data centric by necessity.”

They key to establishing trust is impeccable data governance. As defined by the Data Governance Institute (DGI), data governance is “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” Data governance not only ensures that the data is properly processed and ethically used, it also seeks to align data analytics with business strategy beyond superficial proclamations by promoting a high level of accountability. 

 

Customer-Centric Data Pipelines

Before you can use data to enhance decision making, you have to first identify your data sources. Though some customer data sources will be obvious (i.e. CRM customer service data, sales data, product data, etc.), increasingly organizations are looking towards alternative data sources to better understand their customers and get a leg up against the competition. 

Data pulled from sensors, demographic data, social listening, geo-location and so on can be used to not only a gain a more in-depth understanding of your customers, but also better predictive customer behavior. 

However, be wary of collecting too much data as this can increase storage costs and regulatory risks. Only collect data that is actually of value. 

In addition, data accessed from different external sources will often need to be processed and transformed into a usable, integratable format. Therefore, it is critical that your data governance or general data strategy addresses the building and maintaining of customer data pipelines - basically the behind-the-scenes plumbing of a data ecosystem. 

Obviously your cloud strategy for data analytics intersects deeply with mapping out and optimizing customer data pipelines. This is just one reason why you can’t afford to miss Cloud Strategy for Actioning Data virtual event taking place October 26-27 where you’ll gain expert insight on how to action enterprise data & AI to achieve business goals with a modern, future-ready cloud strategy. 

 

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