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Every company seeks to make better decisions driven by data, analytics and relevant context. To support these decisions, companies must make investments that allow employees to use and experiment with a range of available data sources and vendors in a timely manner without being locked into any potential evolutionary dead end. As technology continues to advance and employees now expect the scale of cloud computing, performance to support real-time analysis and access to relevant documents and media as part of their analytic environment.
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These demands force companies to upgrade their data environments over time in order to maintain a competitive advantage, as well as to avoid having to explain to investors that their current data and analytics capabilities are disadvantaged compared to the market at large.
Understandably, analytic investments are one of the most substantial technology investments a company can make, which leads to the participation of a variety of departments in this purchasing process. It is not uncommon to see non-technical executives and departments participate in some aspects of selecting an analytic solution. Yet, it can be difficult at times for executives lacking the technical expertise to both get the information they need and to ask relevant questions to vendors seeking to upgrade an organization’s analytic capabilities.
In the enterprise, there are several departments that are increasingly involved in analytics purchases. Their key concerns generally relate to the selection of a new analytics solution, including the procurement, finance, revenue and operations surrounding the newly selected tool. Here’s a look at why it matters:
The procurement department will always be involved in significant technology purchases, as formal purchasing processes are necessary at any large enterprise. In looking at an analytics solution, procurement departments seek to show their value in controlling costs. This means providing contractual discounts or the ability to trigger discounts based on some sort of usage or business activity.
Procurement will also want to be able to show how a solution is either superior to all other solutions or to clearly show that a solution meets all listed criteria. This includes defining key performance indicators (KPIs) or management by objective (MBO) metrics used to define success and ensuring that the solution’s success is aligned to business success.
Enterprise data needs to make requests around performance, scale and reliability. This can be difficult to concurrently support without bringing multiple best-in-breed solutions, refactoring legacy data solutions, or by making compromises in the flexibility and variety of use cases that can be supported. Frankly, vendors can sometimes help with this process by adding criteria for future facing demands that they are better positioned to support compared to other vendors, but this approach requires alignment between the vendors and stated client needs.
For a procurement department that is considering analytic investments: Align any new and significant analytics and database contracts (as well as any other significant software and data investments that add new data sources to the enterprise) to existing business intelligence and key software contracts. This way, new data management capabilities will support existing data, analytics and machine learning technologies. This becomes increasingly complicated as the typical billion-dollar revenue business now supports over 900 applications over its network. Additionally, be sure to provide MBOs to analytics vendors to determine how analytic performance and outputs can be aligned to the business.
The finance and accounting departments will always look at analytics in terms of cost. However, this limited perspective ignores the elevated state that the CFO now has within the business. In the majority of businesses, the CFO is treated as a top-two or top-three executive based on their visibility to the top and bottom lines and the role of managing cash as a core strategic role.
This means that the value of analytics for the CFO goes far beyond the pure cost of the solution as real-time analytics provides the CFO with the ability to potentially close the books, support public and investor reporting demands and support strategic forecasting scenarios across multiple entities, countries and currencies. In speaking with finance executives, the role of analytics in supporting strategic business perspectives across sales, marketing, supply chain, operations, talent and succession planning, treasury, intercompany consolidation and investor relations will be top of mind.
In addition, analytics solutions must still provide guidance in the language of business: capital expenditures (CapEx), operational expenditures (OpEx), total cost of ownership (TCO), return on investment (ROI), payback period, internal rate of return (IRR) and the potential predictability of cost and return. These metrics all matter because they provide a consistent standard for comparing all projects across technology, operations, revenue, human resources and other departments based on expected financial impact. Some of these metrics are dependent on the delivery of the project, such as OpEx vs. CapEx. Value-based metrics are often dependent on the believability of the value being proposed or the organization’s ability to execute on the value being stated.
For finance departments considering analytic investments: Look at how an analytics solution will help support strategic views of data, including real-time support for the insights that drive board decisions, product investments and revenue improvement. Avoid vendor lockin and include a “wish-list” of metrics that would accelerate the organization’s ability to make big decisions, such as business unit creation, expansion, or retirement.
From a more tactical perspective, look at the value of analytics based on projects that can be practically deliverable within a two-to-three year time based on current or readily available skills and resources. Include at least one quick win that can be accomplished within the first year that provides meaningful contribution to the ROI of the project. And, putting on the strategy hat, finance professionals should also consider the need for managing compliance and governance for all data under a single platform or control plane to avoid the inevitable complexities of audits, compliance, governance, lineage and unit-based economics for digital applications and services.
Third, revenue-driving departments will always be interested in the role of analytics to help qualify and close sales. The role of analytics in helping to quantify the potential revenue associated with known potential customers is well documented, but sales and marketing departments are aware that the majority of a buyer’s journey occurs before a potential buyer ever speaks with a sales representative. With this in mind, it is increasingly important for revenue-supporting departments to gain visibility to non-sales interactions across marketing, service and other line-of-business departments to understand how contacts are either interacting or are not following up with the company.
From a practical perspective, this means that sales and marketing stakeholders need to ensure that any new data investments support all the data that helps support campaigns and sales processes, including relevant personalization, automation, environmental, economic and cyclical topics that can potentially affect the ability and willingness to purchase.
Recommendations for revenue-based departments considering analytics departments: Don’t settle for basic visibility to existing customer relationship management (CRM) and marketing campaign data from an analytics perspective. Consider the key issues that come into play in slowing down or abandoning sales, including weather, publicly reported financial records, relevant government policy, training issues associated with email and call quality and frequency and other environmental and ecosystem concerns.
This means potentially analyzing a variety of activities, documents, calls, conferences, videos and other requests associated with marketing and sales. The goal should be to provide guidance and forecasting that lines up with regular sales meetings to help the revenue team at all times, not just to support formal reports at the end of the month or the end of the quarter. From a service perspective, this ability to provide a common and shared version of data-driven truth allows companies to see the customer journey and propensity to buy from initial contact to ongoing support.
Finally, operations, supply chain and logistics departments should also make sure that they are included in analytics selection processes, especially as the supply chain is now top of mind in the business world in light of geopolitical stresses and resulting shortages. This is an opportunity to translate manually tracked metrics across plants, remote offices and field locations into more automated methods for data collection and analysis.
However, to fully capture the context associated with manually collected data, analytic solutions may have to collect time-series, geolocation, connected graph and other non-standard data. This requires supporting analytic access to large volumes of data to create appropriate reports and to provide guidance to all stakeholders. In addition, by digitizing this data, companies may gain additional insights by being able to combine operational data that was previously either siloed or off-line with more traditional enterprise applications.
Recommendations for operational departments considering new analytic solutions: The operational needs for data include a wide variety of formats which are often outside the visibility of the stakeholders that most typically considered as analytic stakeholders: IT, data, finance and sales. Make sure that your needs for highly available and high-performance analytics, especially for location and time-based analysis, are supported by any new analytics investment. In the 2020s where cloud computing is readily available and Moore’s Law continues to make computing more available, it should no longer be necessary to wait for hours to transform data or run a query to get the answer you are looking for, no matter how complicated it is. Life is short and computing is cheap: be demanding with your new analytics solutions. In addition, make sure that data-driven insights can be supported anywhere based on the computing and end-user interfaces available across all working locations.
Across all of these areas, business stakeholders consistently see the need to support distributed and varied data sources and face the potential for having to support data that may be stored in a public cloud, private cloud, internally hosted server, or even specific edge computing or end-user devices.
Businesses should look for data architectures that explicitly support providing access to distributed data sources across a hybrid cloud environment on platforms that don’t care what your preferred technical infrastructure or preferred cloud vendor may be. Companies need a distributed data platform that is complex enough to handle any data, any source, any computing platform to provide the simplicity of having business users choose to access what they need whenever they need it. This concept is currently being described as a distributed data cloud, which has the following qualities:
Business stakeholders pulled into a discussion regarding analytic solutions may find themselves overwhelmed by the technical jargon that inevitably is needed to describe the technologies involved. My hope is that the guidance provided in this blog will help business users to stay grounded, be better equipped to analyze solutions based on their particular expertise and select analytics solutions while providing value to the business at large.
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