Making Data Analytics “Real” to Drive Impact

It is evident why data analytics should be used. In general, companies using data analytics claim 8% improvements in profit, 10% decreases in operating expenses, and 30% average annual growth rates. Just one in six businesses successfully employ data analytics, but those that do report margin improvements of 500 basis points or more in a year.

Companies who have embraced artificial intelligence and strategically apply it have the highest profit margins across all industries (Exhibit 1). The benefit is most noticeable in financial services, where strategic AI users typically have profit margins above 10% (compared to negative profit margins for other businesses), and in health care, where strategic users typically have profit margins above 15% (compared to losses for non-adopters of AI).

Exhibit 1

Yet, business executives believe they have a lot of space to grow in their capacity to use data analytics to generate value. More than half of the industrial organizations polled (58%) in a 2022 LNS Research survey on "analytics that matter" stated they were either experimenting or had already implemented an analytics program. However, just 17% of the businesses that responded claimed to be "seeing dramatic business impacts" from their analytics program. Our experience working with industrial organizations has shown us a number of reasons why these businesses are having trouble using analytics and where improvements could have a quantifiable impact on their performance.

Where Industrial Companies Fall Short

Businesses, particularly those in the industrial sector, frequently encounter issues with analytics in four main areas: data impact, data aggregation, data cleaning, and data connection (Exhibit 2). For a variety of reasons, including overestimating their present demands or the competencies necessary in certain areas and underestimating their needs in others.

Exhibit 2

Getting Analytics Right

Businesses that derive value from data analytics use a playbook to handle every one of these aspects. Enhancements to data aggregation do not necessitate a total reorganization of the IT department. ERP system modifications are probably going to be small. Companies can build an application-based architecture on top of existing legacy systems rather than completely overhauling them (Exhibit 3). Each facility's spending, headcount, and customer transaction data would be included in the old system, along with data for finance systems. Data for head count, revenue, materials inventories, and non-labor spending would all be combined into a data lake. The application layer's procedures, which are intended to provide actionable data for diagnostics, continuous operations, and real-time decision tools, would be informed by this.

Exhibit 3

Acquiring better data cleanup begins with realizing that bad data does not have to "hold hostage" the firm. There are no quick remedies for cleaning up the customer master; it takes hard work. Still, it is feasible. If the business concentrates on high-value areas, such as organizing the master customer list to examine margins and discounts per channel at the customer level, the effort is more likely to be effective. It should also cover data architecture in the short- and long-term, which includes data collection, storage, transformation, and consumption.

Decision makers must agree on how data and analysis will apply to different categories of decisions (e.g., what price to quote for a certain SKU to a customer, what discounts to offer, when to pay a certain supplier, etc.) and what improvements in decisions they can pursue in order to improve the impact of data. Next, the business must devote time and resources to creating tools for in-the-moment decision-making, such as a pricing tool. Companies could strive to reinforce capacity by using a build, operate, transfer (BOT) strategy with outside partners, rather than hiring data scientists to develop such solutions.

Data analytics can deliver significant value, but this is not guaranteed. Companies that succeed report margin improvements of 500 basis points or more in 12 months, but only one in six companies succeed with deployment. To get the use of data analytics right, industrial companies need to know the high-value areas where data analytics can add real value to the business; often, these include customer experience, sales, pricing, and labor. To enjoy the benefits as soon as possible, companies can work now on the foundation: start fixing data infrastructure in the near term and establish data governance for the medium term. Finally, companies should close the loop to impact by measuring transparency against the plan’s definition of what good looks like, tracking the net impact of improvement initiatives on the P&L and ensuring that the company takes actions as needed to close the gap.