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Descriptive analytics 101: What happened?

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Descriptive analytics 101: What happened?


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This piece on descriptive analytics is the second in a series of guest posts written by Dan Vesset, Group Vice President of the Analytics and Information Management market research and advisory practice at IDC.

Analytics solutions ultimately aim to provide better decision support — so that humans can make better decisions augmented by relevant information. Decision support capabilities can be segmented into five related categories, each of which is deployed to answer different types of questions:

  • Planning analytics: What is our plan?
  • Descriptive analytics: What happened?
  • Diagnostic analytics: Why did it happen?
  • Predictive analytics: What will happen next?
  • Prescriptive analytics: What should be done about it?

In this series of blog posts, we’ll address each of these analytics capabilities. For a fuller introduction to the topic as a whole, see the first post in the series. This second post will focus on descriptive analytics.

Descriptive analytics: What happened?

As discussed in the previous post, once you have identified your plan, then you move onto figuring out what is happening in your business. That’s where descriptive analytics comes in. Descriptive analytics help answer the question ‘What happened?’ in all its forms: What were our sales last quarter or last month or yesterday? Which customers required the most customer service help? Which product had the most defects? Questions like these form the foundation for your entire analytics strategy. For now, regardless of the smarts built into the technology, we still need people to ask those initial questions and to set the goals and associated key performance indicators (KPIs) by which the enterprise will be measured and managed.

Descriptive analytics have frequently been associated with data visualization via reports, dashboards, and scorecards. Compelling visualizations and an intuitive user interface that adapts to various types of decision makers can help drive pervasive adoption of analytics technology. However, visualization on its own is only one of several functions of descriptive analytics.

The functions delivered by descriptive analytics solutions fall broadly into five categories:

  • State business metrics: Determine which metrics are important for evaluating performance against business goals. Some goal examples would be to increase revenue, reduce costs, improve operational efficiency, and measure productivity. Each goal must have associated KPIs to help monitor achievement.
  • Identify data required: Business data is located in many different sources within the enterprise, including systems of record, databases, desktops, and shadow IT repositories. To measure accurately against KPIs, companies must catalog and prepare the correct data sources to extract the needed data and calculate metrics based on current state of the business.
  • Extract and prepare data: Data must be prepared for analysis. Deduplication, transformation, and cleansing are a few examples of the data preparation steps that need to take place prior to analysis. Often, this is the most time-consuming and labor-intensive step, requiring up to 80% of an analyst’s time,but it is critical for ensuring accuracy.
  • Analyze data: Data analysts can create models and run analyses such as summary statistics, clustering, and regression analysis on the data to determine patterns and measure performance. Key metrics are calculated and compared with stated business goals to evaluate performance based on historical results. Data scientists often use open source tools like R and Python to programmatically analyze and visualize data.
  • Present data: Results of the analytics are usually presented to stakeholders in the form of charts and graphs. This is where the data visualization mentioned earlier comes into play. BI tools give users the ability to present data visually in a way that non-data analysts can understand. Many self-service data visualization tools also enable business users to create their own visualizations and manipulate the output.

It’s important to emphasize that the success of modern descriptive analytics hinges on KPI governance. In today’s business environment of constant change, enterprises must be able to establish and evaluate a portfolio of changing KPIs. In a 2017 IDC study conducted with 120 chief analytics officers, we found that in the past 12–24 months, 65% of them started tracking and measuring new KPIs on behalf of their business constituents. This behavior is one of the most telling signs of digital transformation because it showcases willingness to challenge the status quo and to ask new questions. That said, it also raises the issues of tracking, monitoring, and adjusting KPIs on an ongoing basis.

Governance is also the foundation of trust in data. Coming to agreement on the meaning of data, and the subsequent need to train end users on what the data represents are key to the diffusion of analytics solutions. Without governance, there may not be consensus regarding what the data means, thus guaranteeing analytics a marginal role in decision making.

We find that, when decision making is based on unarticulated data and metrics, decisions are made in an environment of strategic ambiguity. Decision makers understand each other less than they think they do. In data-intensive decision making, coordination is accomplished through consistent interpretation of the data. In our interviews with enterprises across different industries and countries, data and KPI governance is one of the most frequently mentioned challenges of analytics projects.

Although data science- and AI-enabled predictive and prescriptive analytics garner most of today’s headlines, without solutions to answer “what happened?” it is impossible to proceed to the next steps of identifying why something happened, what might happen next, and what to do about it. All other analytics depend on descriptive analytics to establish a common language grounded in business metrics and a roadmap for applying the other types of analytics.

For IBM’s view on the Analytics Cycle, check out our smartpaper, “How Can You Trust Your Data Without the Big Picture?

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