Are You Making Sense Of The Information You Have?

Have you watched the movie Moneyball? Not a baseball fan? Well, neither am I.

But, if you are running your own business, or if you are interested in being more productive, maybe you should give it a go.

The movie narrates the events that took place in 2002 at Oakland Athletics, when general manager Billy Beane and his new colleague, statistician Peter Brand, started following a new way of drafting amateur baseball players to overcome their financial constraints as a team.

The movie, based on a true story, explains how the strategy for drafting the team players moved from relying on the scouts’ gut feel and intuition to using baseball statistics on in-game activity, which led them to build a successful team by hiring undervalued, affordable players.

This approach raised much objection at the time and went against everything the industry believed in.

Yet, after seeing the results, the approach was used fully or in part by other baseball teams, and was even adopted beyond the sport.

Data minefield 

Analysis of readily available data, if used correctly, can do wonders on the results of our actions.

Oftentimes, we have rich a data minefield beneath our feet. Sometimes we are aware of its existence and sometimes we are not; sometimes we think that one day we could potentially mine it, and sometimes we only do so to a certain extent.

The reality is that the majority of times, larger organisations would have the resources to make the most of the data they have, whereas smaller companies would not have the time, expertise or resources to look into this.

Yet, this wealth of data could really change the game and propel them into the higher league, if only it is tapped into and used wisely.

Having a strategy to collect, analyse and use the data that emerges from all parts of the business is crucial, and is something that is worth spending time to think through and set the right processes in place.

Identifying potential data collection 

Big data is the talk of the day.

CoinMetro’s chief executive officer, Kevin Murcko in an article for Entrepreneur.com says that, “Users of big data have typically been large enterprises who can afford to hire data scientists to churn the information. But now, thanks to democratisation of tech and the rise of blockchain, there are tools that can be used by small- and medium-sized companies to both gather big data and to use it to make good business decisions – decisions that will help them be competitive and grow.”

The kind of data you choose to collect can vary significantly.

However, basic ones may include transactional data for customers, such as what and how much was purchased, when they did so, what promotions they used, and how they paid for their purchases.

Big data allows you to set business strategies.

For example, as a coffee shop owner, you may choose to use big data to define which location may yield the highest traffic of people looking to sit for a drink or a slice of cake.

In addition to that, the demographics of the people that frequent the place, as well as interests in terms of style and even music preferences.

And there’s definitely space and enormous use in that.

First things first, why not start looking at data that you can analyse from your own experiences and interactions?

Let’s revisit the example of the coffee shop owner. The coffee shop is already open, and a group of regulars frequent the location.

There’s much that can be learnt and improved by collecting and analysing data from existing interactions.

Starting from peak hours, to the type of crowd that comes to the coffee shop, to the orders that they make, the type of activities they perform in the coffee shop, even the number of new people they bring along, the data is endless.

Going further, you can look at their background as well as other interests, and what it is that attracts them to the coffee shop.

Their interactions with other customers and your staff will also be able to tell you so much. Areas where analysis may bring significant improvement include customer and workforce excellence, process excellence, as well as product improvement

Feasible way to collect data 

Image | 123rf

There’s definitely much hype lately on the Internet of Things and Big Data. However, let’s not forget that long before the Internet, people have already been looking at data collection and information as a way to make decisions.

For smaller businesses, when big expensive technologies are not an option, it may mean that they need to start from the basics – for example, manual observation and tracking with notes or an excel sheet, of things that are going on.

It may sound tedious; however, if an organisation has a clear idea on the kind of information they are looking for and how this can assist them, it makes it all worth it.

When looking to implement a strategy that works for your organisation, you need to look at the type of data you need to collect, your points of collection, and what is the best possible way to collect this, in an efficient, effective manner, that is also cost-appropriate.

A simple strategy of getting your coffee shop customers to sign an evaluation form or sign up for some kind of membership package with special rewards, may provide you with great information: you get data on the back end on what they buy, how often they do it, the location where they shop, preferences, patterns, and so much more.

READ: Nancy Duarte’s Tips On How To Tell A Story Through Data

Having an analysis plan 

Once data is collected, it needs to be analysed. Planning what to do with the insights that emerge will provide the impact desired in any given scenario.

Some ideas to keep in mind are how to share the information with all employees, so that it helps them improve in what they do, how to relook the products and services you offer to get them closer to what your customers want, and how to amend your processes so you become more effective in your work.

Most importantly, how to make it contribute towards customer excellence, which will lead to happier customers who are more willing to spend with you.

Case studies 

Here are some examples of how data analysis has helped other companies:

1. Airbnb 

Airbnb, a company that started back in 2008 when two roommates could not afford to pay rent, is a great example of how using data can make a difference for even the smallest of companies.

How did the Airbnb founders know what information to analyse? Well, they started with an assumption – a hypothesis that photos make a difference in the number of customers drawn.

They went on to test this, sending 20 photographers in 2011 to take professional pictures of the hosts’ homes. Looking at the numbers on the website, this effect was so positive, that they went on to increase the number of professional shoots conducted every month.

The company’s founders still see this strategy as fundamental to their success, and they did this by looking at available data from their users and experimenting with solutions.

Based on that, they took a risk and invested time and effort into monitoring and analysing the numbers – and when they did, they saw results.

2. Dickey’s Barbeque Pit 

Data science company, datapine, uses the example of a United States-based restaurant chain, Dickey’s Barbeque Pit, to explain how restaurants can benefit from data collection and analysis.

The implementation of data collection processes led them to have certain insights that changed the way they approach sales and marketing: their average lunch guest is a 43-year-old male, drives a sport utility vehicle to work, with an average commute time of 30 minutes.

Based on this, they specifically targeted Ford owners who lived 15–30 minutes away from the restaurants in their advertising.

• Women with their children frequent their outlets on Wednesdays for long lunches. This led to them advertising “Craft Wednesdays” on Pinterest to draw more mothers with their children.
• Many customers love fantasy football and dogs.

Because of this, the restaurant started advertising on fantasy football sites, as well as dog lover sites and television channels. They also started using dogs in their catering photos.

Image | pixabay

READ: Understanding Your Customers Is Crucial For Business Growth

Transitioning into a data-driven organisation

We spoke to Dr Farouk Abdullah, chief data scientist at Natural Intelligence Solutions on how smaller companies can start thinking about data collection for their organisations.

1. If an organisation is looking to start making data-driven decisions based on current information they are sitting on, where should they start?

A good starting point for any organisation is to understand the business question, strategy, direction or objective. Especially for small- to medium-sized enterprises (SMEs), the owner or manager needs to formulate the right questions that will drive the business.

Typically, this means stepping away from daily operations and taking the time to think about, develop and plan the business direction.

For example, if you are an electrical component supplier, you could be, “the cheapest in the market”, “the most value for money”, “the most reliable delivery”, “the biggest supplier in Malaysia”, “the biggest supplier in the region”, etc.

The information needed for each of these business directions will be different. Hence, make sure you have an agreed business direction before you look at the data you have.

2. How does an organisation identify which information would be useful for it based on its current needs?

There is always a balance between operational needs and business growth.

Operational information is usually the data or information you would require to run your business – keeping the lights on.

Business growth, however, is focused on what other information you need to grow the business, for example, relating to new product development or market expansion.

A good method to follow is the MoSCoW method of data or information needs. Put simply, this is information that you Must have, Should have, Could have, or Want to have.

Hence, when you are thinking about identifying the information you need for your business, start with the business problem you are trying to solve and then proceed to go through the MoSCoW method to identify the information you must collect and analyse (your business will not survive if you don’t have this information), you should utilise (additional information within your business that may help answer your question), could make use of (information you could buy) or want to incorporate (typically used to answer business growth questions, e.g. market and competitor study for a new market).  

3. How can organisations collect information today that could potentially help them tomorrow with their future needs?

The biggest challenge for any organisations – big or small – is to stay relevant.

Ask yourself if you will still be in business in the next two to three years. Or, if a new technology, competitor or change in customer needs will put you out of business.

Now think about how you may be able to mitigate that.

4. Once organisations identify the information needed, how do they set up processes to collect the information?

If you do not invest in the data capture points, you will have no data with which analysis can help you.

Remember that every interaction with suppliers, customers, banks and employees are data collection points. One way is to digitalise your operational data capture points.

Are you still relying on paper purchase orders, invoices and delivery notes?

If you are and you would like to analyse the time difference between order taken to delivery to payment, you would need to digitise the information collected on those three paper copies.

So, the process could be manual at the start – for example, by getting a clerk to key in the relevant information on the purchase orders, invoices or delivery notes (focus on the MoSCoW method to understand what you want to collect from the documents) – with the intention and plans of moving to more digital technologies such as digitising your order processes including delivery and tracking tools.

Of course, the latter is a more expensive option, but may be worth investing in!