Getting a Rapid Start on the Path to Future Ready
Future-Ready companies are more profitable and more valuable than their industry peers are. Such companies provide great customer experiences and have excellent operating functions, achieved by properly using modern technologies to run their businesses.¹ There are many paths that companies can take to become Future Ready, but any pathway taken will be a challenging journey. The good news is that there is a way for companies to make strides towards becoming Future Ready both quickly and affordably: establishing a digital platform that promptly facilitates business intelligence and automation.
Today’s most successful businesses each have an underlying digital platform that contains all of their business’s information in one central location. We call such a digital platform a Common Data Model (CDM). Effective ones are structured as single sources of truth about the overall business, and they contain information company leaders agree on and understand.
Companies that have established Common Data Models begin to improve their performance by developing a strong business intelligence (BI) capability so they can learn which activities are profitable – and which ones aren’t. They follow that learning by using the CDM to systematically target both existing and potential customers while automating operating functions. The most successful companies ultimately go on to manage their companies digitally by means of real-time enterprises.
Companies operationalize the BI function by developing and deploying key performance indicators (KPIs). KPIs enable companies to react promptly to changes in the operating environment and to decide which aspects of the business to prioritize and which aspects to change. Well designed KPIs enable company leaders to set reasonable targets as the organization goes through the stages of digital maturation. The company can then (1) smartly target its best customer types with products and/or services that generate the highest returns and (2) optimize the use of its resources. Upon achieving those basic capabilities, the company can continuously evolve its digital capabilities and further modernize its decision making and its business operations.
To a great extent, less mature companies are continuing to rely on antiquated business models and intuition instead of data. Their operations also tend to be heavily siloed, with different parts of the business focused on and performing various tasks largely independently. Information sharing across such organizations is relatively ineffective. Their operating structures are usually decentralized, with separate systems for individual applications rather than with a single, unified architecture for the entire enterprise. As those companies try to grow and to share information internally, they’re forced to accommodate each immediate need by wiring together their siloed systems. One of the results of that method is an ineffective and convoluted system layout that hinders the company’s ability to scale the business at a profit. Another result comes in the form of declining revenues and profits.²
FIGURE 1: STAGES OF DIGITAL MATURITY
Source: AlixPartners
Initial Steps to Future Ready
As explained in the previous article “Why Digital Matters: The Road to Future Ready,” it is critical that companies become digitally mature if they are to compete effectively. Many business leaders are ready to put their companies on the path to digital maturity but are unsure where—or how—to begin. Through AlixPartners’ extensive experience digitally transforming companies, the best first step in this process is to establish a Common Data Model and create comprehensive business intelligence that gets applied in informed decision making and that automates processes based on real-time information (figure 1).
A Common Data Model is the underlying digital platform that gives a company the technical capability to make great strides toward becoming Future Ready.
The CDM is where internal and external information about a company can:
- Get collectively gathered and used so as to have near-real-time information about business performance, and
- Be applied to systematically target customers and automate operating tasks (figure 2).
FIGURE 2: A COMMON DATA MODEL ENABLES USE OF BUSINESS INTELLIGENCE AND AUTOMATION
Source: AlixPartners
For instance, consider a company that:
- Has information about how its competitors price each of their products,
- Learns which promotions work with different types of customers and which do not, and
- Has supporting elasticity analytics for calculating optimal pricing to maximize margins.
Furthermore, taking all of that to the next step, the company has an algorithm that calculates ideal prices given all of those considerations, that recommends promotions by types of customers, and that dynamically sets prices. In this example, the company is using business intelligence to both learn how to maximize profits and automate the price-setting process based on that information.
Example: Retailer of branded products
Information available: Competitors’ prices, past promotion performance, and elasticity analytics across customer types and geographies
Business intelligence and automation: Systematic determination of best prices as well as when and how to offer promotions
Common Data Model – In Weeks
Simply put, having all of the relevant data about your business in one place and knowing how to use it are vital to taking advantage of the information in order to improve profits. Businesses have many software applications in place that support various parts of their businesses such as customer order and service applications, customer relationship management tools, and accounting and payroll systems. Depending on the industry and the company, there can be many more types. The ability to use all of the information together and take proper actions with it is where significant additional value gets generated.
Building a Common Data Model can be accomplished in a matter of weeks with today’s technology, the involvement of the right people, and a proven approach. Technology facilitates the connection of source systems directly to a data lake environment so that information from all applications can be collected together in one place on a near-real-time basis. External data can be added too, so that it can be used for broader views of company performance in relation to customer types and competitors.
FIGURE 3: A DATA LAKE COMPILES AND TRANSFORMS DATA FROM MULTIPLE SOURCES
Source: DataBricks
A CDM is built with data lake technology, which holds big data from many sources in a raw, granular format. The data gets transformed so that it can provide regularly updated information that in turn gets used in operating the business. In addition, whenever a business question gets raised, the data lake can be queried for relevant information, and that smaller set of data can then be analyzed to help answer the question (figure 3).
Modern data lakes are cloud based (which means no hardware investments are needed), and they have affordable software costs—especially considering the returns that come from having a Common Data Model asset.
Promptly Establish Business Intelligence and Automation
While a company is developing its CDM, it can create and start to operate comprehensive business intelligence functions in as little as two or three months. That’s the time typically needed to set up near- real-time reports and dashboards that display comprehensive key performance indicators (KPIs) that business operations can put to effective use. From that solid foundation, a company can then move on to full digital transformation, wherein sales and operations get optimized by way of digital methods.
An additional immediate benefit of the Common Data Model is its ability to automate operating functions quickly and affordably. The low-hanging fruit lies in tasks performed by humans that can be done faster, more accurately, and at lower costs by computers. The overuse of spreadsheets in business adds excessive costs and incurs risks. Examples of businesses that have suffered financial losses from errors related to poor spreadsheet governance are innumerable and significant.
As an example of using a CDM for rapid automation, consider a healthcare provider that:
- Gets paid different rates for its services because of multiple different payers across patients, and
- In turn pays its clinicians under various terms—but ones that are tied to patient visits and collections.
Such a situation represents a complex data exercise that requires numerous people across many days when the activities are conducted manually via spreadsheets. Instead, a proper algorithm completes the exercise within minutes.
Example: Healthcare services with thousands of clinicians across hundreds of facilities
Information available: Patient-visit transactions from medical billing system, payer rates data from numerous commercial insurance payers, clinician-contracts systems, and terms data connected with payroll and accounting systems
Business intelligence and automation: Systematic calculation of revenue according to generally accepted accounting principles and amount to be paid to each clinician based on patient transactions, revenues, and collections
Readily available technology makes it possible for companies to quickly and affordably establish their Common Data Platforms. In the next installment of this series, we’ll take a closer look at how a company can quickly achieve business intelligence capabilities by developing and automating its KPIs that help it make prompt and information-based decisions that are sure to maximize profitability.
- Weill, Peter and Woerner, Stephanie. “Update on the Four Pathways to Future Ready”, MIT CISR, MIT CISR Research Briefing Volume XXI, Number 2, February 2021
- Ibid.
Meade Monger, founder of Dallas-based CenturyGoal, is an expert in corporate restructurings, transformations and digital strategy. He is currently a PhD candidate in healthcare research.
