Every success story is built on a series of good decisions. Each successful business constantly evaluates information to make the best possible decision at all levels, from everyday business operations like finding the best shipping materials to top-level strategies like identifying upcoming risks and opportunities in the market.
Making good business decisions depends on three factors:
- Having the right information. Without the right information, the decision-making process is just guesswork.
- Interpreting the information. To prevent confusion and misunderstandings, it’s critical to filter out noise through statistical analysis and distill information into accurate, actionable insights.
- Making the right call. While business analytics can lead to informed decision-making, it’s no substitute for executive judgment. Even with a clear understanding of the right information, good decision-making requires good calls.
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Poor Decision Making Leads to Business Disasters
For each success story, there are hundreds of failures due to decision-making. Most are relatively unknown, but a few spectacular failures – the Kodaks, Blockbusters, and Motorolas of the world – have become standout examples of what not to do. They demonstrate that without a proper business intelligence tool, even large, well-run companies can be blindsided by seemingly-obvious trends.
Let’s look into what happened in a few of these disasters.
Kodak: In with the Old
The downfall of Kodak wasn’t due to the rise of the digital camera – at least, not directly. Kodak actually made significant investments into early digital photography, and for a while was a major player in that market.
Kodak’s failure was due to its stubborn attachment to photo-printing. It paid little attention to the rise of social media and instead viewed digital photography as an opportunity to expand its photo-printing business.
With better decision intelligence, Kodak might have noticed that customers had lost interest in print photos before it was too late, and it might have recognized the need for a digital transformation.
Blockbuster: Crashing a Rickety Ship
Many attribute the fall of Blockbuster to the rise of streaming video. While streaming was certainly its death knell, that was only the case because of a long string of poor business decisions going back to the mid-1990s.
Blockbuster’s inflexible business model depended too much on high-rent storefronts. Between high overhead and competition from DVD retailers like Walmart and Target, it only managed to turn a profit for two years between 1996 and 2010.
It was already in a precarious situation, but when Blockbuster declined to buy a startup named Netflix in 2000, it signed its own death warrant. Sleeping on this opportunity soon led to the rise of a rival that quickly proved more flexible, sustainable, profitable – and durable.
Business intelligence couldn’t have prevented the wide variety of lapses in executive judgment that led to Blockbuster’s collapse. However, it may have resulted in more sober day-to-day choices that offered the stability to recover from such major errors.
Motorola: Repeating the Same Mistakes
It’s been said that the operational definition of insanity is doing the same over and over and expecting different results. Cell phone titan Motorola’s sudden collapse was the result of a years-long engagement in operational insanity going back well before cell phones even existed.
Before the cell phone, Motorola was the primary supplier of two-way radios to emergency services and the military. When the rise of cell phones made two-way radios obsolete, it became one of the primary cell phone manufacturers – but instead of pivoting, it split itself into two companies out of a desire to focus on radios, which it still imagined to be its main product.
With every new innovation over the following decade, Motorola remained stuck on the idea that the previous generation of technology was its core product. It split itself into smaller and smaller business units and was eventually forced to sell off those businesses until there was almost nothing left.
With better business intelligence, Motorola might have been able to assess what its core business was based on data instead of gut feelings and make the hard decisions needed to evolve.
Toys R Us: Sleepwalking Into Bankruptcy
The collapse of Toys R Us was mostly due to the failure to act in the face of change. For a long time, its convenience and low prices made it the center of gravity around which the toy market revolved. However, it never seemed to realize that by the 2010s, those days were long past.
The rise of convenient online shopping and bargain stores like Walmart put a lot of pressure on the retail sector. Some brick-and-mortar chains stayed afloat by offering online shopping. Others started offering unique in-store experiences to differentiate them from other stores and give customers a reason to show up.
Toys R Us had a business model based on convenience and low prices, but online retailers were convenient, and bargain stores were cheaper. Outdone from all directions, it simply failed to offer any reasons for customers to show up.
With good business intelligence practices, it’s likely that Toys R Us would have survived. These practices would have set off alarm bells in its headquarters by the early 2000s, and it very likely would have shifted its strategy instead of sleepwalking into bankruptcy.
Avoid Mistakes with Decision Intelligence from Pyramid Analytics
The art of decision-making is especially important in our rapidly-changing business world. We still can’t see the future, but thanks to the increasing availability of data and technologies to analyze it, we are closer than ever before. How?
Say hello to decision intelligence.
Decision intelligence is what’s next in analytics. It uses artificial intelligence to overcome the shortfalls of business intelligence and makes enterprise-level analytics not just possible, but the new gold standard.
Machine learning-powered decision intelligence will enable your teams to:
- Speed up insights by providing instant access to any data at scale.
- Scale adoption because you can deploy AI-driven experiences for any person.
- Simplify analytics to support any analytics needs.
Interested? If so, we recommend some additional reading.