The 4 Personas for Data Analytics

Due to new modernization strategies, data analytics is architected from  top down or through the lens of the consumers of the data. In this blog, I will describe the four roles that are integral to the data lifecycle. These are the personas who interact with data while uncovering and deploying insights as they explore this organizational data.

Citizen analysts/knowledge workers

A knowledge worker is primarily a subject-matter expert (SME) in a specific area of business—for example, a business analyst focused on risk or fraud, a marketing analyst aiming to build out new offers or someone who works to drive efficiencies into the supply chain. These users do not know where or how data is stored, or how to build an ETL flow or a machine learning algorithm. They simply want to access information on demand, driving analysis from their base of expertise, and create visualizations. They are the users of offerings like the Watson Analytics.

Data scientists

Data scientists can do a more sophisticated analysis, find a root cause to a problem, and develop a solution based on an insight that he discovers. They can use SPSS, SAS, etc or open source tools with built-in data shaping and point-and-click machine learning to manipulate large amount of data.

Data engineers

They focus enable data integrations, connections (plumbing) and data quality. They do the underlying enablement that a data scientist and citizen analyst depend on. They typically depend on solutions like DataWorks Forge to access multiple data source and to transform them within a fully managed service.

Application developers

Application developers are responsible for making analytics algorithms actionable within a business process, generally supported by a production system. Beginning with the analytics algorithms built by citizen analysts or data scientists, they work with the final data model representation created by data engineers, building an application that ties into the overall business process. They use something like Bluemix development platform and APIs for the individual data and analytics services.

Putting it all together

Image a scenario where a Citizen analyst notices (from a dashboard) that retail sales are down for the quarter. She pulls up Watson Analytics and uses it to discover that the underlying problem is specific to a category of goods and services in store in a specific region. But she needs more help to find the exact cause and a remedy.

She engages her data scientists and engineer. They discuss the need to pull in more data than just the transactional data the business analyst already has access to, specifically weather, social, and IoT data from the stores. The data engineer helps create the necessary access – the data scientists can then form and test various hypothesis using different analytic models.

Once the data scientist determines the root cause, he then shares the model with the developer who can then leverage it to improve the company’s mobile apps and websites to be more responsive in real-time to address the issue. The citizen analyst also shares the insight with the marketing department so they can take corrective action.



How Software is Changing the way we do our Business

Today information pours in faster than we can make sense of it. It’s being authored by billions of people and flowing from a trillion intelligent devices, sensors and all manner of instrumented objects.And with 80% of new data growth existing as unstructured content – from music files, to 3D images, to medical records, to email keystrokes – the challenge is trying to pull it all together and make sense of it.

But what if you could tap into those information to uncover lucrative business opportunities or avoid some risks? The tools that I have been working on helps our Clients in finding highly intelligent and profitable answers in clever analytics software that can organize, store and mine all of the information scattered throughout their organization and provide customized intelligence to gain faster insight from this information. Here is an example of it…

There was a fictional scenario that some folks created in which a hospital needed to find ways to control costly readmissions, specifically readmissions of cardiology patients. It was implemented using IBM InfoSphere(the product that I work ), IBM Content and Predictive Analytics and Cognos Business Intelligence.

What was interesting was how the IBM technologies worked together to support this effort: IBM InfoSphere to make sure that hospital’s store of patient and operational data was trustworthy; the new IBM Content and Predictive Analytics for Healthcare solution to sift through treatment records to identify key, differentiating characteristics of at-risk patients; and IBM Cognos Business Intelligence to deliver this information in an easily consumable way – to hospital staff on the go and as they interact with patients.

So what was the learning?

  • Sometimes the right metric is out there – the value comes when you use it. We need to help our customers think differently about how to apply analytics to their business challenges.
  • Analytics definitely provides a competitive advantage – when an organization is willing to set aside traditional ways of making decisions. This change can be harder to make in some organizations than in others. But today, even many sports organizations are taking an analytics-based approach to managing their talent!

A nice link to follow up…

Disclaimer: The postings on this site are my own and don’t necessarily represent IBM’s positions, strategies or opinions