Developing an Analytics Strategy Starts with Data Governance and Business Intelligence

An important differentiation between data governance, business intelligence, business analytics cognitive analytics and predictive analytics is needed as a basis for building a digital supply chain strategy.  Every organization needs to define for themselves the differences between these terms, and not just bend to how external consultants are professing to position their views on these concepts.

Data Governance” is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.[1] The basic components of data governance ensure the split of accountability and responsibility related to data thus empowering better decision making while using data from disparate data sources and methods. In effect, data governance provides a system of decision rights and accountabilities for the information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.[2]

A data governance program can provide many benefits, including,

• Increasing the value of your existing data by identifying ways to utilize it.
• Enhancing existing processes and build additional processes that work better
• Decreasing the cost of managing data through synergies with other organizations
• Standardizing policies, standards, procedures and systems related to data
• Providing ways to resolve existing problems related to data (such as quality, availability, security etc.)
• Improving transparency through socialization, dissemination and creation of awareness
• Ensuring better compliance, security and privacy
• Increasing revenue through improved customer-facing responsiveness
• Enable better decision making in the end to end supply chain
• Reducing organizational strains related to data issues

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers and other corporate end users make informed business decisions. Sporadic use of the term business intelligence dates back to at least the 1860s, but consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella phrase for applying data analysis techniques to support business decision-making processes. What came to be known as BI tools evolved from earlier, often mainframe-based analytical systems, such as decision support systems and executive information systems.[3]  Typically, business intelligence can be used for ad hoc analysis using visualization tools.

Analytics is the outcome of a series of advanced operations performed on data extracted from business intelligence systems. Business analytics may include dashboards, visual graphics, charts, etc. that are developed using tools such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.  In many cases, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.

Gartner notes that the BI and analytics platform market is undergoing a fundamental shift. During the past ten years, BI platform investments have largely been in IT-led consolidation and standardization projects for large scale systems of record reporting. These have tended to be highly governed and centralized, where IT-authored production reports were pushed out to inform a broad array of information consumers and analysts. Now, a wider range of business users are demanding access to interactive styles of analysis and insights from advanced analytics, without requiring them to have IT or data science skills. As demand from business users for pervasive access to data discovery capabilities grows, IT wants to deliver on this requirement without sacrificing governance.[4]

Gartner also notes that as “…companies implement a more decentralized and bimodal governed data discovery approach to BI, business users and analysts are also demanding access to self service capabilities beyond data discovery and interactive visualization of IT curated data sources. This includes access to sophisticated, yet business user accessible, data preparation tools. Business users are also looking for easier and faster ways to discover relevant patterns and insights in data.”

According to a recent study by the International Institute of Analytics and the SAS Institute[5], BI adoption is more prevalent across the organization than advanced analytics. They note that “While the path from basic reporting to more advanced analytics work is often considered as a shift from BI to AA (Advanced Analytics), the reality is that advanced capabilities should augment, not replace, less advanced functionality.”  The reasons stated for this include criticality to business, recognition of benefits and utilization in strategy. Organizational weaknesses are perceived to be one of the strongest deterrents to the adoption of BI and advanced analytics practices across organizations. Data Governance programs are fundamental to both BI and BA outcomes, as it is critical to ensure acceptable data quality levels.

In many companies, pockets of analytics practice have developed in a random and disjointed way. Organizations need to develop a strategy for development of BI platforms to create advanced analytics, but in a structured and planned fashion that allows the greatest flexibility for multiple business units to conduct their own functional analytics, using a common and trusted source of data.

A variety of opinions, debates and points of view have emerged regarding the differentiation between BI and BA. For example, experts have claimed that BI is a noun and BA is a verb, that BI is backward-looking and that BA is forward-looking, and that BI is needed to run the business while BA is needed to change the business.[7]  Discussions can also wander into data structure and quality, internal or external analytics, or customer vs. supplier focused analytics.  Due to the confusion of issues, it is imperative that a common framework be established within any organization to provide a common language for creation of a strategic vision of the future.

[1] DAMA UK Working, Group. (2013, October). The Six Primary Dimensions for Data Quality Assessment. Retrieved from http://www.damauk.org/rw/CatViewLeafPublic.php?&cat=403

[2] Thomas, G. (n.d.). How to use the DGI Data Governance Framework to configure your program. Retrieved from http://www.datagovernance.com/wp-content/uploads/sites/29/2014/11/wp_how_to_use_the_dgi_data_governance_framework.pdf

[3] http://searchbusinessanalytics.techtarget.com/definition/business-intelligence-BI

[4] Gartner, “Magic Quadrant for Business Intelligence and Analytics Platforms” 23 February 2015 ID:G00270380.

[5] International Institute for Analytics. (2016). IIA Business Intelligence and Analytics Capability report. Retrieved from http://iianalytics.com/analytics-resources/2016-business-intelligence-and-analytics-capabilities-report