The difference between stated and actual analytics capabilities is significant enough to require a specific evaluation in a merger or acquisition
The increased importance of data and analytics in many companies’ core products and services, coupled with increased activity in mergers and acquisitions, makes evaluating an acquisition target’s analytics capabilities a crucial step of the process.
But there’s evidence—as found in our review of 40 recent acquisition targets—that companies overstate their analytics maturity, causing an “analytics gap” between real and stated capabilities.
Evaluating a target’s analytics capabilities offers a way forward that identifies and maps the analytics gap in detail, allowing private equity and other acquiring firms to make more informed decisions and look ahead to planned capabilities. Managing the analytics gap is a difficult yet crucial step for both private equity firms and acquisitive companies.
The emergence of big data, analytics, and artificial intelligence (AI) across industries in the last decade has led to an explosion of companies seeking to build analytics maturity and more fully leverage the value of data. This trend has disrupted many industries and often challenged legacy enterprises to compete due to new “Digital Native” business models originally being founded on data.
The rise of analytics has generated significant buzz and marketing, particularly around AI. Gartner recently characterized the “hype” around analytics and AI, as evidenced by the millions of marketing dollars spent on the topic by the big consultancies:
“Artificial intelligence (AI) is ‘hot’ and hyped. CIOs, AI, data and analytics leaders across many industries are seeking breakthroughs, which will come in the long run. For now, though, they should focus on finding practical uses for AI that will have immediate impact.”
In step with the rapid growth of analytics, both public and private companies are acquiring firms with analytics and AI capabilities, seeking to capitalize on the emerging market. In particular, private equity funds have grown significantly and with similar growth as the analytics services market, far outpacing U.S. GDP growth. Private equity-backed technology is one of the fastest-growing business sectors, with some indications that the sector has dry powder.
We assess analytics capabilities across many types of acquisition targets to support the diligence process, with the flexibility to focus more strongly on key areas of concern and opportunity.
We perform more than 400 technology diligences each year across industries, the vast majority of which support private equity firms evaluating an acquisition target. In such diligences, our technology and industry experts serve as “house inspectors” to determine the validity of a target’s claims across their technology infrastructure. Primary focus areas include application security, software development processes, platform performance, and technical integration of prior acquisitions.
An increasing number of private equity firms have expressed interest in assessing a target’s analytics capabilities, reflecting their view of the increased importance of analytics in acquiring firms’ investment theses and target business models. To meet this need, a typical analytics diligence includes a review of target-provided documents and target interviews to assess a target’s current capabilities, record of execution, and value creation. We recommend that the analytics diligence process cover a broad range of capabilities that include data management, reporting, business intelligence (BI), advanced analytics such as machine learning, and analytics organizational effectiveness.
One of the key features of the methodology is the flexibility to evaluate companies of different analytics maturity: A target focused on operational reporting can be analyzed for reporting tools and processes, while another target with AI-centric products can be evaluated for predictive model effectiveness and differentiation.
Some targets require more depth of assessment due to the often-sprawling nature of their technology solutions.
Prior to engaging the target directly, the diligence process begins with reviewing the confidential information memorandum (CIM) developed by the target and often their M&A broker-advisors. This marketing document, usually 50 to 100 pages, summarizes a target’s market opportunity, products, finances, technical capabilities, and strategic direction.
The CIM often includes strong statements of the target’s use of analytics, particularly when analytics is core to the target’s products and services. Comparing the CIM to the assessed analytics capabilities is a primary outcome of the analytics diligence process, which includes reviewing additional documents and interviewing the target company’s leadership team.
It often becomes apparent during the diligence process that analytics capabilities advertised in the CIM are not aligned to the diligence’s findings. We refer to this difference between the target’s marketing and our assessment of analytics capabilities as the “analytics gap.” We see three primary patterns in the overstatement of analytics and AI capabilities in targets’ CIMs that lead to these analytics gaps:
In our experience, the gap sometimes arises due to misunderstandings around analytics at leadership levels that are driving the M&A process. For example, a small target company in a nascent industry for analytics might believe its capabilities to be more mature than they are, such as portraying standard reports as more sophisticated.
Such companies also often struggle more with translating analytics into business value, such as incorporating key customer insights into real business processes.
Analytics gaps also sometimes arise because owing to their “hype” nature, analytics and AI can be more easily overstated or exaggerated without being uncovered until the diligence process begins (and analytics scope is included). While outright misrepresentation likely isn’t the intent, and target companies aren’t incented to exaggerate too much, the marketing inherent in the process can lead to strong analytics gaps.
To quantify the analytics gap between the CIM and our findings during the diligence, we compiled 40 recent acquisition targets from 2018-19 in which the acquiring company elected to include analytics in our diligence scope. We gauged both stated analytics capabilities and our analytics diligence findings for the following categories.
Internal and external reporting processes, quality of management reporting, level of data integration to support reporting, and impact of reporting on business decisions
Use of advanced tools, platforms, and interactive visualizations to support business insights, enable self-service access to insights across business functions, and empower quicker business decisions
Extent to which analytics capabilities are implemented in production processes and/or embedded in the acquisition target’s products
Advanced analytics and data science capabilities to inform operations and generate business value
Existence and maturity of analytics leadership and teams, and level of analytics maturity of the acquisition target as a whole (operating model, strategy in place, etc.)
For the five categories, we evaluated the analytics capabilities stated in each target company’s CIM and assessed during the diligence and assigned a simple two-point analytics maturity score for each: 0 = none, 1 = weak capabilities, 2 = strong capabilities. We also calculated a composite maturity score for each target by adding the scores across the five analytics categories, with a maximum value of 10 points.
For one example, a target’s CIM claimed strong use of machine learning in its products, but the diligence process uncovered little actual use implemented in current products. In this case, the Predictive Analytics and ML/AI analytics maturity scores were determined to be 2 for the CIM and 1 for our assessment.
The graphic below compares the composite maturity score based on the CIM (“Target Marketing”) with the score based on the analytics diligence (“West Monroe Assessment”). The analytics gap is reflected in the difference between these two scores. On an individual-target basis (left side of the graphic) the analytics gap varies but is consistently below the equal line. Averaging all 40 targets in the analysis leads to an overall gap of 2.2 points: Roughly 30% of their marketed analytics and AI capabilities overstate actual capabilities in our analysis.
Stepping into the individual analytics capabilities, the chart below shows an interesting trend when going from less advanced (reporting, BI) to more advanced (analytics products, use of predictive) analytics: The size of the gap increases for more advanced analytics capabilities. We believe this partly reflects an “AI bubble” in the analytics market, where some targets elected to include advanced analytics terms such as machine learning to garner investment attention.
In a few recent target companies that emphasized machine learning in their CIMs and marketing documents, we found no machine-learning capabilities during the analytics diligence. Instead, rules-based decision engines or manually coded process automation were in place, with machine learning on their product road maps. In some cases, such analytics capabilities might be years away from being firmly established, potentially impacting a target’s value over the investment horizon.
Organizational effectiveness is another key component of the analytics gap. This likely has two additional causes beyond the target’s tendency to overstate analytics capabilities: First, a shortage of data scientists, paired with a common lack of commitment to hire them, leads target companies to have fewer analytics resources than their CIM implies. Second, analytics maturity across a company’s functions (marketing, sales, operations, etc.) can vary widely, and lack of analytics leaders and strategic commitment will create barriers for a data-driven culture.
To investigate the analytics gap for different types of targets, the 40 companies in our analysis were separated into two groups: “Digital Natives” and “Non-Digital Natives.”
A Digital Native firm is typically a few years old, often uses modern technologies (e.g. cloud), and sometimes is founded for the purpose of monetizing data. Non-Digital Natives are often older or in less analytically mature industries such as healthcare, and they sometimes struggle with data and digital. More than half of the Non-Digital Natives in our analysis are in healthcare.
Figure 5 compares the average maturity gaps between Digital Natives and Non-Digital Natives for four analytics capability categories, with more advanced analytics from left to right. Non-Digital Natives exhibit larger analytics gaps for more advanced capabilities, such as Analytics in Products & Production and Predictive Analytics and ML/ AI. Given the increasing gaps for Non-Digital Natives for more sophisticated analytics, it’s evident that companies are more likely to overstate or misrepresent capabilities when they’re less likely to have those capabilities.
We believe the differences in the graphic below arise for two reasons: First, Non-Digital Natives likely struggle to understand analytics, increasing the possibility of misstating their capabilities. Second, Non-Digital Natives are more likely to follow a traditional, linear path along the analytics maturity curve. For example, a machine- learning application in a traditional hospital system might not make sense if there are no historical reports telling hospital administrators what happened the previous day.
The gap for Reporting Capabilities is slightly larger for Digital Natives, possibly indicating that Digital Native firms prioritize traditional reporting less than value- creating opportunities with advanced analytics. For example, a platform may be built to analyze contracts using natural-language processing and assign risk scores to contracts. BI and reporting are then built to understand the risk scores in the context of a contract management workflow.
For acquiring firms seeking to better understand a target’s analytics capabilities and wanting to improve their evaluation of the target, here are five recommended actions to take throughout the diligence and acquisition process to navigate the analytics gap:
Given the crucial importance of the technical diligence of companies involved in a potential transaction, the ease with which analytics capabilities can be overstated or exaggerated leads to the analytics gap we presented in this paper. In particular, the “gold rush” nature of advanced analytics such as machine learning and AI, and the desire for acquisition targets to portray themselves as AI-enabled, drive the need for careful evaluation during the diligence process.
In many cases, we have observed that a lack of understanding of analytics among target leaders and stakeholders, particularly for Non-Digital Native companies and industries, can contribute strongly to the analytics gap. An opportunity exists for advisors of target companies to play a larger role in ensuring a company’s stated capabilities better match true capabilities, which would improve the diligence process overall.
As one bright spot, Digital Native show smaller analytics gaps, likely due to business models based on data and less technical debt. We’ve also seen recent examples of confidential information memoranda with reduced boasting of analytics and more precise language on capabilities, likely as a result of increased scrutiny and the increased analytics maturity of all firms in the transaction.
The growth of analytics is not slowing down any time soon, and the excitement around this growth is clouding expectations for many executives. To navigate the process, acquisitive companies and private equity firms need heightened awareness and a smart game plan for analytics. Over time, the increased prevalence of analytics across all industries should reduce the analytics gap. In the meantime, we recommend deeper evaluation and vigilance.
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