An excerpt from the forthcoming book “Organizing and Managing Insanely Great Products” by David Fradin with RN Prasad
Every product enterprise, no matter whether they are organized as a hierarchy, networked or matrix structure, will have executive making decisions every day.These decisions could be central to their role/ responsibility or associated with business support functions like HR/ Finance/ Marketing/ Product support/ R & D and so on.The tug of war that happens in a traditional organization is the choice between gut-feel (based on years of experience) and the information available from IT applications. Some of the examples of such decisions may include:
● Allocating right budget for marketing campaigns
● Allocating right resources for innovation & R&D programs
● Choosing business partners, teams for product excellence rewards
● Assigning right level of discounts based on customer buying profiles
● Acting on social media voice of product users & influencers
● Choosing optimal levels of stock of products and spares
Fact-based decision making is a systematic process that emphasizes collection of right data, ensure quality of data, perform non-judgmental analysis to extract insights, collaboratively deliberate the pros and cons of possible decisions and choose business decisions that are supported by the analysis results rather than guesswork, thumb rule or hunch.
If data driven decisions yield better results, why are companies not adopting them? Let’s explore five factors that inhibit people from making fact-based decisions. The following may not be an exhaustive listing of all possible factors, but a trigger to think on these lines.
● The decision makers are informed after the decision is made by executive Management to introduce ‘Business Intelligence Reporting’. (No early involvement – No commitment)
● Team has NOT examined the entire information value chain to determine readiness of the organization to embrace ‘fact based decision making’
● Focusing on people before bringing in a specific technology tool (like self-service)
● Fact based decision making culture is NOT considered as a program is driven by the enterprise leadership team.
As I have discussed elsewhere in this book, the decision by senior management to cancel my product line at Apple is an example of the first two factors just mentioned. One of my clients developed and launched a $40 Million VOIP system which failed because the VP just wanted to do it without consulting with his people.
The reasons why facts drive better decisions include – the ability of the computer to find non-obvious answers, ability to crunch large datasets very fast, can take into account hundreds of market variables and ability to adopt a methodical repeatable process for analysis. Business analytics coupled with effective data management techniques will provide timely, accurate and actionable insights to decision makers. As the outcomes of the decisions becomes more and more reliable and predictable, managers become more and more comfortable using fact based decision support systems.
Business analytics includes all the software and processes that enable business enterprises to apply metrics-based decision making to all functions ranging from finance/HR/marketing to supply chain and customer relationship management. Business insights derived through analytics has significantly helped business enterprises globally to find answers to business challenges like fraud/ non-compliance, inefficient operations, increasing time-to-profitability, pricing pressures, customer attrition, shrinking market share. Most business enterprises have adopted a three stage approach to convert raw data to business outcomes- Data to information followed by information to insights and ending with insights to business outcomes.
Today, businesses need to have transparency in decision making while working in the industry ecosystem as part of compliance. The actions taken by executives based on the insights have proved to be very effective and responsive. Some of the areas where business enterprises face challenges while implementing fact based decision making programs include:
● Data – Common challenges we find in product enterprises around data include timely availability of data needed for analysis, a tool to assess the quality of data & fix it, effort needed to integrate data from many sources and bring to one standard format as well as non-availability of data required for analysis itself.
● Processes – Many product enterprises may not have standard processes and tools for data analysis and could be handled only by the IT function. Decision makers requests for data may be fulfilled reactively with possible delays. Many enterprises may not have process to systematically define KPIs and convert the insights into dashboards or scorecards.
● Technology – Business data today is the form of big data (un-structured) as well as structured and distributed forms. Product enterprises may not have acquired new generation tools for data management including acquisition, quality management, analysis, dashboarding and storytelling. In addition decision makers demand information to be provided on mobile devices, on cloud or on portal and in highly personalized forms including self-service.
● Organization – Many product enterprises may not have the right talent needed to implement complex analytical projects, management support or investment constraints to roll-out enterprise wide analytics solutions.
Some of the factors that will enable decision makers embrace fact based decision making culture in product enterprise includes:
● Easy access to data, associated with the role
● Executive leadership emphasizing the need for unbiased & transparent decisions
● Standardization of KPIs for roles
● Internal analytics centre of excellence to help LOBs/ SSUs leverage analytics
● Standardization of tools for data analysis and visualization
● Dedicates support desk for data integration and exchange support
● Adoption of information security initiatives driving compliance
● Adoption of secure mobile devices support and cloud analytics adoption
● Personalization of information delivery and avoiding of information overload
● Support for multiple analytical workloads such as BI, exploration, predictive analytics, big data analytics etc.
● Development of data catalogue that provides quick access to authorized pieces of enterprise data assets (including metadata). Alternatively, data virtualization helps business or non-technical users to use standardized business views of pre-integrated data.
● Development of reusable dashboard templates, report templates that users could customize themselves.
● Educating decision makers at all levels about the power of analytics.