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Analytics in the Middle Market

“The future is already here, it is just not evenly distributed.”

William Gibson

Overview

The question of analytics, or more specifically how to deploy the tools and capabilities now broadly available for analyzing large data sets, distill actionable insights from that analysis, validate insights and implement the changes necessary to realize the potential that the analysis has uncovered, is a pressing one for the middle market. This question is pressing because the middle market is awash in data. However, the middle market lags far behind other segments of the economy in taking concerted action to seize the opportunity that analytics presents.

In 2021, analytics represents something truly rare: a broadly available but individually distinctive opportunity to improve the operations and enhance the strategic positioning of nearly every company in the middle market.

Multiple Points of Equilibrium

Experienced business transformation professionals know that organizations exist in a world of multiple points of equilibrium. A successful business transformation can be fairly described as the process of migrating an organization from one point of equilibrium to another, with the condition that the new point of equilibrium will possess superior return characteristics. Essentially, a successful business transformation results in an upward shift of the efficient frontier for a company. In 2021, analytics represents something truly rare: a broadly available but individually distinctive opportunity to improve the operations and enhance the strategic positioning of nearly every company in the middle market.

A successful business transformation requires identifying, refining, and applying levers to a business model in order to generate an outsized degree of positive change relative to the resources employed. Every business era has a go-to set of tools and capabilities to which the gaze of leaders inevitably drifts as they search for such a lever. In 2021, analytics is near the top of the list. Consequently, the development of an in-house analytics capability should be a focus for every middle market leadership team focused on value creation.

Implementation is the Goal

A depressingly easy way to stump a data scientist candidate is to ask them to map out a path from identifying a prospective avenue of inquiry through to the realization of measurable business improvement.

Generally, there is a grudging acceptance that time will be spent on data wrangling, or the process of cleaning and reformatting data for ease of analysis. There is a marked level of excitement in the discussion of the tools and techniques that can then be applied to the cleaned and reformatted data. Enthusiasm then dips when discussing the presentation of insights to those outside the data science sphere, although there may be a slight uptick when discussing data visualization.

Any discussion of the fate of analytics insights beyond the presentation stage represents something of a chasm among analytics professionals. On one side are the majority who are uninterested and unenthusiastic (they did their part), and on the other are the small minority who understand that insight without implementation has little value. This latter group works to become not just excellent technicians but able communicators and translators, helping to bridge gaps in understanding and foster the development of implementation paths for insights generated by the analytics team.

Analytics as a Lever

Marshalling the full potential of analytics in driving a successful business transformation requires a clear understanding of not only the potential but also the limits of these tools and capabilities and the organizational bottlenecks that inevitably manifest as middle market companies seek to fully exploit them. This challenge is further compounded in the middle market by resource constraints, a deficit of analytics savvy among management and leadership, and the communication challenges that are sadly persistent between analytics teams and all other members of a company.

The discrete steps that allow companies to best mitigate these challenges can be broken down into two groups: 1) Define the Problem, and 2) Path to Realization.

The challenge is that, while analysis scales, implementation does not. And results, not multivariate regressions, are the goal.

Define the Problem

Many impressive quotes have been attributed to Albert Einstein, but my favorite, perhaps, is this one: “If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”

Modern analytics tools and techniques are incredibly powerful, and when deployed against the right data sets, with a clear understanding of the goal, they can and will produce impressive results in terms of insights. However, ensuring that an analytics capability results in not only insights but measurable business improvement requires the discipline to more rigorously define the problem, not only in its technical elements but the business problem as well, and to act only then, having taken the necessary steps to maximize the expected outcome of your efforts.

Data Mastery

The necessary condition for the development of an effective in-house analytics capability is a mastery of an organization’s own data. The phrase “garbage in, garbage out” has never been more apt than it is in our analytics age. The simplicity of this point belies just how profound it is. Nimble startups designed with a high level of data savvy and enormous multinational companies able to devote immense sums to digital transformation initiatives may have clear paths to data mastery, but for middle market companies, the path is anything but clear.

Target Selection

The tools now available and in wide use by data analytics staff and consultants create an illusory sense of boundless possibility when choosing targets. The challenge is that, while analysis scales, implementation does not. And results, not multivariate regressions, are the goal. The scalability of analytics tools has a tendency to blind even senior level data science practitioners to the many difficulties of implementation. The application of brute force algorithms to large data sets as a path to insights will inevitably yield results that are underwhelming. Choosing avenues of inquiry with care is essential, especially when seeking quick wins to build internal support for an in-house analytics team. Effective target selection requires that the analytics team not be siloed but be integrated into business operations. This can be as simple as including data analysts in key meetings and involving the analytics team in reviews of business unit performance. In the end, an analytics capability is meant to confer a business advantage for a company, and this will only happen if the analytics team is not walled off from the rest of the business.

 

Define the Question

After defining the data source(s) and target for an analysis, it is vital to define the question. This step is crucial in that it requires a nuanced understanding of the business. By investing the time necessary in defining the question, an analytics effort increases the likelihood that any insights generated will have a clear path to implementation.

Analysis is cheap, and implementation is a bottleneck. It is far easier, and cheaper, to test and refine the algorithms rigorously than it is to prematurely move ahead with the implementation of real-world changes in the business necessary to realize projected efficiencies.

Scrutinize

For middle market companies seeking to build an analytics capability, it can be easy to be lulled into complacency once there is a clear path to the generation of insights. This is a trap. Insights are not the proper endpoint of an effective analytics capability. Rather, insights are a stop along the way. A properly integrated analytics team will not only generate, but rigorously test and screen insights, with only the most promising being passed along for further investigation. Analysis is cheap, and implementation is a bottleneck. It is far easier, and cheaper, to test and refine the algorithms rigorously than it is to prematurely move ahead with the implementation of real-world changes in the business necessary to realize projected efficiencies.

 

Integrate

Insights that have been thoroughly vetted should be presented to leadership for review, with cross-functional implementation teams then formed to validate their real-world potential. Each insight becomes a project, with its own staffing requirements, timeline, and ROI characteristics. Resource constraints will necessitate that only the most promising insights will be acted on initially. This will ensure that only the most only high-return prospects are acted upon. Additionally, through detailed review of a targeted set of insights, company leadership will give itself much needed time to begin to integrate consideration of insights from the analytics team into the existing rhythms of company decision making, or change those rhythms, as appropriate.

 

Implement

Effective implementation will require a high level of communication between the analytics, business unit, and functional leaders in a company. This level of integration will initially feel awkward and forced for all parties, but over time it will become apparent that the returns to the company of the analytics staff having a direct line of communication with key decision makers will foster a higher level of business understanding in the initial stages of analysis, and a higher level of analytics understanding in insight review, yielding a virtuous cycle of improvement in the company’s ability to successfully transmute insights into enhanced business performance.

Conclusion

The middle market is traditionally a segment that is at best a fast follower of technology trends, and at worst a reluctant adopter of them. 2021 represents a crucible year, with broad opportunities and challenges for many middle market companies. There are few broadly applicable, high-return investment opportunities available for middle market companies, but investment in an analytics capability is one of them. Properly executed, such an investment can reset a company’s equilibrium point, permanently raising its level of profitability, with all the attendant value creation that such a shift implies. Like all business transformation opportunities, realizing this potential will not be easy, but the middle market companies that successfully pursue this path will find themselves richly rewarded.

About the Author

David Johnson (@TurnaroundDavid) is Founder and Managing Partner of Abraxas Group, a boutique advisory firm focused on providing transformational leadership to middle market companies in transition.  Over the course of his career David has served as financial advisor and interim executive to dozens of middle market companies.  David is also a recognized thought leader on the topics of business transformation, change management, interim leadership, restructuring, turnaround, and value creation.  He can be contacted at: david@abraxasgp.com.

Implementation: Big Data & Analytics Bottleneck

 

“In five years, inside the enterprise, analytics is just going to be called ‘management.’”

 –        David Wagner, “Five Intuitive Predictions About Analytics”

In recent years the explosion of big data and analytics tools has been truly inspiring.  Companies and academics are now able to mine data sets that are mind bogglingly large.  This wealth of data has proven to be a goldmine as data scientists hunt for previously unrecognized relationships, and seek to optimize everything from the most promising routes of inquiry for drug development, to the pricing of consumer goods, to best practices for maintenance of industrial machinery.  Machine learning algorithms, making use of these large stores of data, are powering rapid advancements in artificial intelligence.  Nevertheless, this multi billion dollar market has, at its heart, a fatal flaw: data by itself is useless, and insights without an action plan are nearly so.  Absent change management expertise and leadership, the promise of big data will never be realized across the broad swath of organizations that might otherwise benefit from it.

As big data and data analytics tools progress through the hype cycle, disillusionment is setting in as company leaders struggle to realize the massive potential of these capabilities across massive organizations.  And it is here that big data runs into a fundamental challenge: analysis may scale, but actionable insights do not seem to, and insights alone do not guarantee successful implementation.

Savvy companies, recognizing this fact, are seeking to embed data scientists into their management teams.  This is a step in the right direction, but it is unlikely to be sufficient for companies with extensive physical operations and well-established business processes.  For these companies, data-driven insights that suggest a compelling benefit to be gained from a reorganization of the business are worse than useless: they are a cruel promise of a gain that cannot be achieved, yet another example of technology’s reach exceeding its grasp.

What is needed is data savvy change management, spearheaded by leadership with the ability to foster a data-driven culture while also building a capability for change and reinvention into the very DNA of established companies.  The promise of big data will, in the end, be realized or not based on the availability of a relatively scarce resource: accomplished change agent leadership.

About the Author

David Johnson (@TurnaroundDavid) is Founder and Managing Partner of Abraxas Group, a boutique advisory firm focused on providing transformational leadership to middle market companies in transition.  Over the course of his career David has served as financial advisor and interim executive to dozens of middle market companies.  David is also a recognized thought leader on the topics of business transformation, change management, interim leadership, restructuring, turnaround, and value creation.  He can be contacted at: david@abraxasgp.com.

Big Data and Organizational Fluidity

This article also appeared in Business Insider

The term “Big Data” and the unfortunate hype surrounding it obscures a crucial development in the management of organizations, regardless of size. We have definitively moved into an era of copious data, and the challenge for all stakeholders in an organization is to find ways to analyze that data, discern actionable insights from the analysis, implement changes based on those insights and analyze new data in order to measure actual versus forecast results. The days of analysis being anything other than a core feature of the day-to-day operations of an organization are over; we have entered a period of continuous, iterative change.

This new world calls for experimentation as a central operating tenet. The organizations that thrive in coming years will be those capable of embracing this new fluidity.

Gold in the Terabytes

For capital providers, there are outsized returns to be generated by seizing the opportunity that our new, data immersed age offers.  The wealth of data offers tantalizing prospects, as savvy management teams and their advisors push those companies willing to make the effort into a virtuous cycle of continuous improvement, identifying compelling growth opportunities, driving margin improvement, eliminating unnecessary expenditures, and overall driving substantial improvements in enterprise value.

We are moving far beyond simple SKU analysis and the development of optimal pricing models.  By understanding high quality data sources both inside and outside and organizations, areas of inefficiency can be relentlessly targeted, and with the continuous stream of data that most organizations generate, small projects that yield results can quickly be scaled up to organization-wide initiatives.

The Certainty of Casualties

Management by rule of thumb is anachronistic; based on what we are seeing in the market we anticipate that those small and mid-sized organizations willing and able to adjust will find a data savvy business model to be a compelling force multiplier for an organization of any size.  Those organizations that fail to adapt will find themselves at a severe and growing disadvantage as their competitors utilize superior insights to grow market share and identify new pockets of opportunity.

Additionally, blind adherence to data will also produce casualties.  Massive data sets are almost by definition “noisy”, and insights derived from such data must take into account both the data’s strengths and limitations.  Ironically, this increasingly analytic field needs more than ever a solid qualitative framework to ensure good “sanity checks” are not forgotten.

About the Author

David Johnson (@TurnaroundDavid) is Founder and Managing Partner of Abraxas Group, a boutique advisory firm focused on providing transformational leadership to middle market companies in transition.  Over the course of his career David has served as financial advisor and interim executive to dozens of middle market companies.  David is also a recognized thought leader on the topics of business transformation, change management, interim leadership, restructuring, turnaround, and value creation.  He can be contacted at: david@abraxasgp.com.

Becoming a Data-Driven Organization

This article originally appeared in the Loftis Consulting Blog

The unrelenting pace of 21st century commerce has resulted in a flood of data that threatens to overwhelm small businesses.  Every company generates a vast quantity of data on sales, marketing, production, ordering, and every aspect of operations.  Most companies are doing nothing with this data, and the failure to make use of it is expanding what is for many small businesses their biggest competitive disadvantage.

Today it is essential for companies to know themselves better than they ever have before.  Management should identify a limited number of key performance indicators (KPIs) that capture the performance of the company, and focus on rigorously tracking those KPIs.  This data-driven approach can initially seem onerous, but the superior insight it provides makes it well worth any initial inconvenience.

Our clients have reaped significant benefits from efforts to shift their organizations to a data-driven mode.  Those benefits include:

  • Early Warning Capability: Poor financial performance is often not the first but the last sign of a problem.  Regular reporting of KPIs can highlight trouble before it impacts the bottom line.
  • Opportunities: Nothing bolsters the case for growth opportunities like data.  Website traffic and keyword search data is valuable market intelligence and analysis of that information has helped our clients identify unvoiced client needs and allowed them to reap the sales benefits of meeting those needs.
  • Profitability: Analysis of raw sales data has permitted Gross margin analyses by customer, region and salesperson in order to identify unattractive customers, unprofitable regions and under-performing salespeople.

We are in a new world, a world driven by data.  With every aspect of a company’s operations producing data, the insights that can be gleaned from capturing, analyzing and acting on that data are increasingly becoming a valuable asset.  Conversely, failure to make use of the data your company generates will hinder profitability, inhibit your ability to react to changes as quickly as competitors and increase the likelihood that growth opportunities will be missed.

About the Author

David Johnson (@TurnaroundDavid) is Founder and Managing Partner of Abraxas Group, a boutique advisory firm focused on providing transformational leadership to middle market companies in transition.  Over the course of his career David has served as financial advisor and interim executive to dozens of middle market companies.  David is also a recognized thought leader on the topics of business transformation, change management, interim leadership, restructuring, turnaround, and value creation.  He can be contacted at: david@abraxasgp.com.