{"id":18154,"date":"2020-03-09T09:56:10","date_gmt":"2020-03-09T08:56:10","guid":{"rendered":"https:\/\/timoelliott.com\/blog\/?page_id=18154"},"modified":"2023-01-23T18:15:15","modified_gmt":"2023-01-23T17:15:15","slug":"why-analytics","status":"publish","type":"page","link":"https:\/\/timoelliott.com\/blog\/about\/an-executive-guide-to-enterprise-analytics-strategy\/why-analytics","title":{"rendered":"Why Analytics?"},"content":{"rendered":"

[under construction — please ignore the mess]<\/p>\n

Why should your organization be interested in analytics in the first place?<\/p>\n

Well, let’s start with a little history: here are some lessons learned from analytics in the past in order to succeed with analytics in the future.<\/p>\n

<\/a>Analytics is as old as the abacus<\/h2>\n

\"\"<\/a>Using calculation devices to increase efficiency, cut costs, and increase profits is as old as the abacus.<\/p>\n

And every computer has been sold with the promise of providing business executives with the real-time information they need to make better decisions\u2014including the very first ones used for business.<\/p>\n

\"leo-608x417.jpg\"<\/a><\/p>\n

A version of Lyons Electronic Office which became the world\u2019s first business computer in 1953 \u2013 and was used to run predictive analytics.<\/em><\/p>\n

<\/a>The first computer for better business decisions<\/h3>\n

In the UK in 1951, the first ever computer designed to manage a business was created. It was baptised LEO, short for Lyons Electronic Office, and the first application it ever ran was analytics.<\/p>\n

Other computers existed at the time. ENIAC, an \u201celectronic brain\u201d had been designed by the US Army to calculate missile trajectories. And IBM was working with MIT on an air defense computer. But Lyon\u2019s management decided that new computer technology could help with business, too<\/p>\n

Lyons & Co. wasn\u2019t a computer company. It didn’t even make calculating machines. Instead, it was famous around the world for cakes, tea, and sandwiches. Its 30,000 employees manufactured 36 miles of swiss roll cakes every day and uniformed staff served over 150 million meals a year in Lyons teashops across the UK.<\/p>\n

The company struggled with an issue that is familiar to anybody running food businesses today\u2014the bakery products were perishable. If they weren\u2019t sold by the end of the day they would be wasted and if they ran out then valuable sales would be lost.<\/p>\n

LEO was designed and built by Lyon\u2019s own engineers to ensure that each teashop would be restocked every day with just the right about of bread rolls, boiled beef, and ice cream.<\/p>\n

And Lyons was excited by the possibility that LEO could help track the performance of each teashop, and each product sold, in order to enable managers to make well-informed decisions.<\/p>\n

The very first job to run on the new computer\u2014which took up around 2,500 square feet of floor space\u2014was \u201cbakery valuations\u201d and by 1954, the system was fully operational and churning out standard reports for managers across the organization.<\/p>\n

<\/a>Actionable information at their fingertips<\/h3>\n

For the first time, managers could see at a glance which products were popular and which were not. LEO handled the conversion of meal orders into the individual items required to make them, such as beef, carrots, and dumplings. And reports meant managers could easily compare the sales value of goods delivered to each store with the revenue for the month to check for efficiency and fraud.<\/p>\n

The system was designed for \u201cmanagement by exception,\u201d with reports on the top ten and worst ten performing stores for different products, so that managers could make adjustments. And they could compare the sales that were forecasted with the amounts that were actually sold to see if store managers were consistently under- or over-estimating demand.<\/p>\n

LEO was even used to track the production quality of products such as the high-quality tea blends on which so much of the company\u2019s reputation was based.<\/p>\n

<\/a>Information meets culture<\/h3>\n

Some of the biggest cultural issues that make analytics deployments difficult today were also present in these early days.<\/p>\n

The first problem was the company\u2019s choice of key performance indicators lead to suboptimal practices. For example, waste was considered the ultimate sin of a store manager. The inevitable result was that the shops often had empty shelves in the afternoon, resulting in untapped revenue potential and dissatisfied customers.<\/p>\n

The second issue was getting top managers to adapt their management style to the new possibilities. The most advanced data processing system in the world was in the hands of a conservative management whose style hadn\u2019t changed since the previous century. The LEO team had designed the system to focus on the key figures required to run the teashops, rather than what the managers themselves had asked for. The managers still liked having their printouts of everything that was happening everywhere.<\/p>\n

<\/a>What history tells us<\/h3>\n

There\u2019s a lot of industry hype about analytics, big data, and artificial intelligence. But it\u2019s clear that the biggest goal of analytics\u2014better decisions\u2014 has remained stable for a long time. And so have the biggest barriers to achieving it.<\/p>\n

\"\"<\/a><\/p>\n

\"ad-for-old-computer-1956-Univac-690px.jpg\"<\/a><\/p>\n

Computer manufacturers have been promising real-time information for executive decision-makers since the 1950s!<\/em><\/p>\n

Analytics isn\u2019t a technology problem<\/h2>\n

It\u2019s a problem of business definitions and processes that aren\u2019t optimized for data flow.<\/p>\n

<\/a>Why can\u2019t I get the data I need?!<\/h2>\n

If LEO was able to provide business information in the 1950s, why is it still so hard today?! It’s because it’s hard, and because\u00a0expectations keep rising.<\/p>\n

<\/a>Technology is hard!<\/h3>\n

Today\u2019s computer systems have become very good at gathering and storing the kinds of data you need to run the business on a day-to-day basis.<\/p>\n

But analyzing that data remains difficult. You need to gather information across multiple systems\u2014not just inside your organization, but also from the market as a whole. You need to be able to compare the data to history in order to get context. Data from different systems will use different terms. Even as technology improves, the amount of data that we want to access and analyze grows with it (and beyond it!).<\/p>\n

<\/a>Expectations constantly rise beyond what is possible<\/h3>\n

\u201cBusiness people will always be dissatisfied with their information systems\u201d
\n<\/em><\/strong><\/p>\n

–Timo’s Law of Analytics<\/p>\n

Survey after survey over the last twenty years has shown that the average satisfaction with corporate information systems has stayed steady, at around 15%<\/p>\n

That doesn’t mean they were a mistake, and it doesn\u2019t mean that the billions invested in analytics systems have been worthless.<\/p>\n

It just means that computer systems have become more sophisticated, but haven\u2019t kept up with executive\u2019s needs. Ultimately, the job of an executive could be defined as \u201cmaking decisions”\u2014and that\u2019s something computers will never do.<\/p>\n

Decision=not enough information<\/h2>\n

A\u00a0decision<\/em>\u00a0is a situation where information is lacking\u00a0by definition.\u00a0<\/em><\/p>\n

Why? Because we only use the word \u201cdecision\u201d when there is some uncertainty and human discretion involved.<\/p>\n

For example, let\u2019s image a simple decision where the information systems have provided all the information currently available to make that decision. There are two possibilities: either the data shows that the choice is obvious, and choosing yes or no is a formality, or there\u2019s some need for reflection before choosing an answer.<\/p>\n

The second option necessarily means that there are some extra factors that must be weighed that the computers cannot or have not provided. This is, by definition, the job of executives.<\/p>\n

Of course, over time, both the data and the processing power of computers gets better, and so they can take over more of what we currently call \u201cdecisions\u201d.<\/p>\n

For example:<\/p>\n