Visual analytics for actionable insights has been added to Bookmarks.
Visual analytics for actionable insights
Diving into big data to lead, navigate, and disrupt
Using visual analytics can help you better understand your organization’s complex data. And the insights you gain can enable you to accelerate performance and gain competitive advantage. Here are four steps for getting started.
- Download the PDF
- View the infographic
- Overcome fundamental hurdles
- Follow the brighter path ahead
- Get in touch
Meaningful analytics for valuable insights
In today’s marketplace, companies are under constant pressure to improve profitability. This is prompting them to seek higher levels of transparency into financial performance and uncover insights that can enhance decision making and create value. Armed with meaningful analytics, business leaders can recommend actions to improve the bottom line—adjusting pricing, reducing product costs, and rationalizing unprofitable products or services.
Of course, in order to quantify value, business leaders need information—specifically, cost data. But with more data collection points than ever before, there can be a glut of information. With too much information to sift through easily, and without a clear understanding of the facts, leaders are often unable to provide insights and recommendations to leadership.
Visual analytics can help.
According to the Deloitte survey of financial executives, Cost transparency: Helping finance create business value, nearly two-thirds of the respondents said the most important function of cost information and related business analytics is supporting strategy and strategic decision making. Leveraging this information to maximize profitability and reduce cost is both a science and an art. And the upside is clear: Organizations that embrace complexity and use visual analytics to better understand their data can accelerate performance and gain competitive advantage.
Overcome fundamental hurdles
As noted in the graphic below, cost information is leveraged to support a range of decisions—from business strategy and operational performance improvement to evaluating the cost to service customers and contract profitability:
The first challenge business leaders experience on the road to profitability management is obtaining meaningful cost information from the myriad data sources that may be available. To tackle the challenge of managing data, companies need a range of business analytics. A wide variety of factors, including increased globalization and years of industry consolidation, have complicated the ability to acquire data to perform analysis. The problem: Data is available, but it may not be easily connected because it’s stored in various locations and systems. For large organizations with multiple business lines, the move to a shared-services model has also made attributing costs to products or customer groups difficult. This is primarily because these service models require organizations to change how costs are captured and then allocated across businesses. Complex supply chains that necessitate ongoing transfer-pricing activities can also make it difficult for companies to get an accurate view of profitability. These complexities foster beliefs that the data is “bad” or too difficult to mine.
Fortunately, there are new tools to help sort it out.
Follow the brighter path ahead
Visual analytics tools and techniques have been developed to aggregate multiple data sets from disparate sources. Unique identifiers, such as product numbers, transaction codes, cost centers, etc., can enable the development of a Common Information Model (CIM) that provides one comprehensive source of the truth. Other business analytics tools have been devised that allow companies to build on and improve these datasets by filling data gaps, cleaning messy fields, reconciling elements across sources, and transforming the underlying structure to support subsequent analysis. Once a slow and labor-intensive process, these steps can be done much faster today, thanks to artificial intelligence, superior processing power, server sizes, and special data-science programs. A combination of powerful technology and advanced techniques are rendering common business analytics problems of the past obsolete.
Many organizations still use spreadsheets to manage data, but that technology has limitations. It’s time to consider visual analytics tools that can transform the data more efficiently and get to the needed level of granularity to develop powerful insights.
So where can you start?
Ask the right questions
Companies must first define what information is needed to have an impact on key business decisions and strategies. Understanding what questions the organization is trying to answer enables the development of more effective and efficient cost and profitability models. The process of defining questions can also provide a guide for visual analytics and data analysis overall.
Specifically, it’s important that questions reflect an understanding of a company’s strategic priorities and a sense of the major pain points that business analytics and profitability data can help alleviate. Typical questions include:
- What’s driving profit performance, and what areas in the business need attention?
- What are the levers to reduce overhead and shared services costs?
- What’s the total cost to serve by customer, channel, or region?
- What’s driving swings in margins?
- How is product mix impacting the business?
Transform the data
The information required to support meaningful analysis, data visualization, and insight generation likely resides in disparate systems with varying degrees of accuracy across sources. The most impactful solution should start with the collection, collation, and transformation of this data into a single data model. To achieve this, common data handling and transformation challenges will need to be overcome. These include missing values, different output formats across systems, and varying levels of granularity for common fields.
Routines in common data processing software can automatically join and cleanse data from different sources. Meanwhile, advanced software packages can intelligently impute missing values and help apply allocation methods that lead to greater levels of granularity. The evolution of data processing software and tools is helping to ensure that existing limitations in source data aren’t permanent impediments to these types of analytics initiatives.
Apply visual analytics
Asking the right questions and transforming requisite data offers a new way to identify potential opportunities and make strategic changes. It allows organizations to go beyond the spreadsheet to data visualization, helping them show cross-functional data and identify what business leaders may not have seen otherwise. This approach is highly graphical, interactive, and visual. It starts with a sketch—sometimes called a wireframe—that maps out what an ideal information dashboard might look like if it were designed to answer all the critical questions the organization has identified. Once this design has been agreed upon, it’s formalized as a finished dashboard.
Basically, visual analytics is the art of creating dashboards and interfaces that display critical data in meaningful, insightful ways. Practical applications beyond cost and profitability management might include forecasting, planning and analysis, risk management, strategic sourcing, operational complexity reductions, and anti-fraud sensing and monitoring—to name a few.
Leverage information into insight
For many organizations, getting cost data to a more granular level is key to informed strategic decision making. The advancement of data transformation tools have helped overcome the historical challenges of disparate systems and incomplete data. Equipped with these tools, as well as visual analytics dashboards, companies can derive the information they need to provide more accurate forecasting, make better strategic decisions, and generate more value from costing data. This information, in turn, can provide companies with insights for managing profitability, maximizing future prosperity, and transforming risk into value across the board.