By Brynne Henn - July 16, 2021
Modern cloud-native organizations have constantly growing streams of raw data flowing from every corner of the enterprise. Determining the impact this data has on business performance can be an overwhelming task requiring teams of analysts. That’s where employing business intelligence (BI) can help.
By presenting current and historical data within a business context, the data insights supplied by BI tools enable organizations to make smarter, more confident decisions that provide strategic direction for years to come.
Instead of relying on intuition and “gut feel,” companies can use BI to find new ways to increase revenue, track performance, boost operational efficiency, identify market trends, expose problems, and much, much more.
Before we dig deeper into the primary types of decisions in business intelligence, let’s define what we mean by decision-making in a business context and understand how business intelligence factors into the process.
Simply put, decision-making is the process of deciding something, especially with a group of people. From a business decisions perspective, the aim is to achieve business objectives to satisfy stakeholder needs and expectations.
For the decision to be effective, however, decision-makers must forecast the outcome of each option and determine which is best for a particular situation. That makes decision support systems (DSS) like decision intelligence and business intelligence absolute essentials.
Business intelligence refers to the technology tools and processes that enable businesses to organize, analyze, and contextualize business data from around the company. Business intelligence tools and decision-making transform raw data into meaningful and actionable information.
BI is the means through which organizations make smarter business decisions. While data fuels the engine, BI-related infrastructure like a data warehouse, dashboards, reports, data discovery tools, and cloud data services make it possible to extract insights from your data.
Companies make big mistakes when they base business decisions on what they think will happen instead of relying on facts.
Using BI and advanced analytics, organizations can extract crucial facts from the mountain of data, transforming it into information companies can act on to make informed strategic decisions. The result: improved business processes, operational efficiency, and business productivity.
Business intelligence decisions typically fall into three categories: strategic, tactical, and operational.
An organization needs to gain a complete understanding of these types of decisions in business intelligence to make better-informed decisions that lead to increased customer and stakeholder satisfaction, operational efficiency, and revenue.
Business intelligence tells you what is currently happening and what happened in the past to bring you to that state.
On the other hand, business analytics is an umbrella term for predictive data analysis techniques (can tell you what’s going to happen) and prescriptive (tells you what you should be doing to create better outcomes).
Using business intelligence and analytics efficiently is the difference between companies that succeed and those that fail in the modern environment.
Business intelligence supports the three types of decision-making mentioned above: strategic, tactical, and operational. Its frequency and organizational impact characterize each.
Strategic decisions comprise the highest level of organizational business decisions, are usually infrequent and made by the organization’s executives. Yet, their impact is enormous and far-reaching.
Some types of strategic decisions include selecting a particular market to penetrate, a company to acquire, or whether to hire additional staff.
Decisions made at this level usually involve significant expenditure. However, they are generally non-repetitive in nature and are taken only after careful analysis and evaluation of many alternatives.
Tactical decisions occur with greater frequency (e.g., weekly or monthly) and fall into the mid-management level. Often, they relate to the implementation of strategic decisions.
Examples of tactical decisions include product price changes, work schedules, departmental reorganization, and similar activities.
The impact of these types of decisions is medium regarding risk to the organization and impact on profitability.
Operational decisions usually happen frequently (e.g., daily or hourly), relate to day-to-day operations of the enterprise, and have a lesser impact on the organization. Operational decisions determine the day-to-day profitability of the business, how effectively it retains customers, or how well it manages risk.
Answering a sales inquiry, approving a quotation, or calculating employee bonuses may be examples of this decision type.
You can summarize these types of decisions in business intelligence this way:
How do you make the best business decisions? Some people trust intuition or gut feel. Others reach out to constituents and experts for advice. Still, others cede decision-making to information systems and automation. However, the smartest business decisions are made by those who look at the numbers.
In a competitive business landscape, where agility, flexibility, and a real-time decision-making process are critical and timely, accurate data analysis is more important than ever. In that respect, relying on the types of decisions in business intelligence is non-negotiable. It is required for long-standing success and market dominance.
In the big data era, information companies expand rapidly, requiring tools and solutions that make sense of the data and help organizations make effective decisions.
Yet many organizations rely exclusively on descriptive BI systems and tools like Looker and Tableau for organizational decision analysis. While these ad hoc tools can help you visualize changes in your data, they are not designed for today’s wide-ranging, rich data and limit the number of possible dimensions you can explore.
Data volume increases force you to spend more time “dumbing down” your data just to work within the constraints of these tools, which leaves less time for valuable analysis.
Sisu is a decision intelligence engine built to work with the messy, complex, and imperfect data you’re already using for analysis today. Sisu can:
Prepare and analyze all your wide, messy data;
Automate key driver analysis and get personalized results over time;
Surface actionable, multi-factor insights, and drill down fast.
Ready to augment your existing BI tools and start transforming your analytics workflow? Schedule a demo to see Sisu in action and learn how customers like Samsung, Mastercard, Wayfair, and Gusto use the platform to drive measurable business value.