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Answering the Call of Data Duty

 

by Alan Ho, Director of Marketing, Asia Pacific, TIBCO Software

As more enterprises work towards a data-driven model for both business and operational decisions, dependencies on using data come increasingly under the spotlight. Even as we gather data in scale from customers and internal stakeholders, relying on data analysts alone to provide the timely business recommendations seems to be a tall order.

Understanding the Basics

The most basic question from key decision-makers within your organization continues to be, “how can I optimize my business with data?” From your data, you should be able to understand what happened, why it happened, what will happen, and what you should do about it. These are the goals organizations should keep front and center on their successful analytics journey.

  • Descriptive – What happened?
  • Diagnostics – Why it happened?
  • Predictive / Machine Learning – What will happen?
  • Prescriptive – What should I do?

In reality, the users and producers of these four categories of analytics are hardly the same. From line-of-business (LOB) stakeholders to data teams and IT staff, each collects and analyses data in a way they understand and will need data sets presented in a consumable format which can meet their different functional needs.

What’s in a Name: Knowing your User Personas

To put your talent requirements into perspective, let’s go beyond the technical needs and deep dive into the business goals of data user groups. Like how we understand our external customers through their buying journey, we do the same for internal customers by developing their Data User Personas.

We identify three categories: (a) Line-of-Business, (b) Data, and (c) IT team.

LOB Data IT
Business leader Analytics leader Technology leaders
Business professionals Data scientists Developers
Business analysts Data engineers
Types of Analytics – Priority
What should I do What will happen What happened
What will happen Why it happened Why it happened
What happened What happened What should I do
Why it happened What should I do  What will happen

We can prioritize the analytical needs of each persona by knowing their immediate business needs. For business leaders, professionals and analysts, the immediacy of their actions is critical to the strategic direction of your organization.

The diagram below also provides a view of what types of data points different personas need to access for their daily tasks. To answer “what should I do,” a business leader needs to see data representation in a consumable manner, already parsed through pre-determined business considerations and provided with actionable steps relevant to keeping the lights on. At this stage, enterprise reporting (ERe) may not drive down to the granular details of how data sets are named and the string details of each column, but these fields will be of interest to the Data team instead. Even within the Data team, a Data Scientist and Data Engineer may access common data reporting, such as Data Visualization (DV) for a broader view of data commonalities, but a Data Engineer may delve into the Master Data (MDM) to analyze unstructured information.

Now we understand that the right mix of tools and data sets can only be used to its best potential when matched to the right people. To put your organization in its optimal data model for analytical success, you can start by tailoring experiences for your identified personas to your data capabilities.

More than Data Visualization

Through our experience working with customers, we know that they need more than data visualization. We identified the emergence of a new segment of analytics users – a more sophisticated, experienced analyst whose needs are unmet by current data discovery tools. They need higher value use cases, as well as broader and deeper analytics capabilities without sacrificing simplicity.

Higher value use cases from the needs of business leaders in an organization demand both expertise in data visualization as well as a capability to provide actionable insights from said data. By deploying a host of software solutions simply to satisfy the thirst for data may not work for a group of business users. The pressing need is to take the science out of preparing and analyzing data while decreasing the time-to-value quotient from your chosen analytics tools. On the flipside, serious players within your organization should not compromise the depth and breadth of data manipulation they can achieve with their specialist skills but should empower them to build even better analytics model with greater data-wrangling capabilities. Essentially, do more with less for users across the spectrum.

Using TIBCO Spotfire, we marry these two needs with built-in data wrangling capabilities, geoanalytics, and easy-to-use predictive analytics. All best-in-class, without the unnecessary fuss.

For a quick introduction to Spotfire, watch this video.

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