10 Ways to Get the Most Out of Business Analytics

Businesses, especially those in the manufacturing sector, have more information than ever before, stored in more systems and locations, being produced in increasingly varied forms and being used in strikingly varied ways. Advances in information technology have fueled this explosive growth creating both opportunity in new ways for manufacturers to reach new markets and customers and complexity in trying to collect, manage and interpret data and into information that can help guide them to success. Technology, a two-sided coin, also can provide tools to handle the complexity in the form of analytics.

Ventana Research recently conducted a study examining business analytics in manufacturing. Participants expressed a number of common concerns regarding the need for and use of business analytics. Most participants are concerned about how well they handle them; only 21 percent are satisfied with their current analytics efforts.

For manufacturers wishing to improve their performance through business analytics, here are 10 recommendations.

Assess the maturity of your business analytics. While the Ventana Research Maturity Index placed 12 percent of respondents at the highest Innovative level in their use of analytics, 60 percent are in the bottom half of the maturity hierarchy. In people-related issues, the index identified lack of skilled resources and lack of executive support. Process-related issues included taking longer than a week to provide metrics from analytics, formally reviewing metrics no more often than quarterly or annually and low prioritization and lack of budget. In information-related issues that negatively impacted business analytics use, the research identified stale, outdated and inaccurate information as well as failing to prioritize basic informational needs. In the category of technology, the research found immature technology that is not working, unsophisticated technology known to be ineffective and a failure to prioritize forward-looking and predictive analytics. These shortcomings all impede a manufacturing organization’s effectiveness and performance and all need to be addressed.

Look for business analytics tools that are easy to use and flexible. Usability and functionality—that is, business capabilities—stand out as manufacturing organizations’ most important considerations in selecting business analytics regardless of company size, individual role or functional area. These should be central focuses in evaluating tools. To be usable and functional, analytics systems must provide a range of options for how to include the information in presentations, which are increasing; participants indicated an interest most often in the standard charts, reports and tables. However, documents, visualizations such as gauges and sliders, text, Web pages and maps were also identified as important by one-third to one half of these companies. Determine which of these are important to your organization today and may be tomorrow.

Look for tools that support a range of roles in a manufacturing environment. The most important capability for an analytics system is to make it possible to search for specific existing answers, rated important or very important by three-fourths of participants. Because anomalies are common in business, individuals need to be able to drill down to find underlying causes. The second-most frequently chosen capability is exploring data underlying analytics, also deemed important or very important by nearly three-fourths. The participants rated similarly (22 percent to 28 percent deemed them very important) four other capabilities: to publish analytics and metrics; to explore data by working with maps, charts and tables; to set alerts and thresholds; and to collaborate in the review of analytics. The most important capability for them, rated by about half as very important, is being able to source data for the analytics; without this capability it’s difficult to compile meaningful analytics. Rated equally important by manufacturing participants was the ability to take action based on the outcome of the analytics (that is, to complete the cycle of measure, decide and act).

Ensure business analytics are widely accessible. In Ventana’s overall research on business analytics, only one-third of senior executives and one-fourth of vice presidents, directors and managers have analytics always available. While it is true that a large majority of executives have most of what they need, this is insufficient for optimally effective performance. Almost nine in 10 manufacturing organizations regard making it simpler to provide analytics and metrics to those who need them as important or very important. Also keep in mind that doing this from mobile devices such as smart phones and tablet computers will only increase in demand; already more than one-third of participants said this is important or very important.

Don’t let inferior data undermine use of business analytics and metrics. Business analytics should be about determining what is happening and will happen to an organization. But the research shows almost seven in 10 manufacturing organizations spend the most time waiting for data, preparing data and reviewing it for quality and consistency. Conversely, only 28 percent spend most of their time on true analysis processes such as assembling scenarios, searching for causes and determining how changes will impact current business. If these preparation obstacles could be addressed, the amount of time people work with analytics could be reduced; currently, 60 percent are spending more than 25 percent of their time with them. Take steps to ensure your source data for analytics is both fresh and correct; if it isn’t, you risk undermining the use of metrics and KPIs as business improvement tools.

Replace spreadsheets as tools for business analytics. Spreadsheets are well established as a tool for analysis in organizations of all kinds and sizes, but they are ineffective for repetitive analyses shared by more than a few people. Yet research shows that along with business intelligence technologies (for querying, reporting and performing analysis) and analytic warehouses and databases, spreadsheets are the tools manufacturing companies most commonly use to generate analytics. Indeed, spreadsheets are used universally in 38 percent and regularly in more than half of these organizations. While they may be familiar, research shows that organizations using spreadsheets least have more accurate, timely data—and they deliver periodic reports about 40 percent sooner. Organizations should limit the use of spreadsheets as data stores and for repetitive analyses, particularly in cases where the results are reported to and used by more than a few people. Their failings, limitations and necessary work-arounds undermine the needs identified by participants to simplify analytics and metrics and ensure technology usability in the process of producing business analytics.

It helps when IT and the lines of business work together on analytics. The research found most people who have primary responsibility for designing and deploying analytics have experience with sophisticated tools. In 53 percent of manufacturing organizations, analytics are designed and deployed by the business intelligence or data warehouse team or by general IT resources. Line-of-business (LOB) analysts are involved in 39 percent of companies; 22 percent use LOB analysts alone and another 17 percent have IT analyst and LOB analyst collaborate.

Understand the value of predictive and forward-looking analytics. Predictive analytics can give a business glimpses of what may happen, the consequences of actions and scenarios for how to respond to change. Technology has advanced to a stage where it is feasible to provide them to a variety of users in manufacturing businesses. Yet the research shows predictive analytics are not yet high-priority analyst capabilities for the lines of business (LOB) nor are what-if and planning-based analytics; each is deemed very important by less than 30 in the LOBs. Exceptions were contact centers, in which predictive analytics ranked second-most important, and supply chains, where they are third-most important. Finance departments are the least likely to use predictive analytics even though they could be widely applicable within this function.

Address barriers standing in the way of improving business analytics and performance. The research shows the most significant barriers to making changes in analytics are fundamental: lack of resources, no budget, a business case that is not strong enough and too low a priority assigned to the effort. What’s more, these barriers are interrelated. Failure to provide a compelling business case results in a project receiving a low priority and therefore not being allocated the resources or budget sufficient to implement the changes.

Resources must be adequate to enable investment in technology to make analytics easy to access and use. Driving change and addressing barriers require understanding the benefits of investments. Demand that vendors show how their products deliver clear benefits such as these and address issues such as total cost of ownership and return on investment that can help lower the barriers in your organization.

Consider cloud computing for deploying for business analytics. Slightly more than half of manufacturing organizations still prefer on-premises deployment for business analytics, but the research found a significant preference for software as a service, or cloud computing. Consider evaluating if your organization is looking to avoid the effort and expense of having in-house technology resources manage your business analytics.

For more read Business Finance's article The Analysis of Data to Gain Business Insight.