Chapter 13
In This Chapter
Moving away from gut-based decisions towards fact-based decisions
Using data to influence your ongoing business strategy
Spotting potential for additional or completely new business models
By collecting and analysing data, you can transform your business in two ways: Firstly, you can use data to examine your existing business model and improve how you do business. This may involve understanding and targeting your customers better, increasing employee well-being, fighting fraud, or improving efficiency. Secondly, there is the possibility that data may eventually change your business model or lead to diversification. For example, if you manufacture products that have the ability to collect a lot of data, you may find that the data itself is more valuable than what it tells you about improving your business – enabling you to sell that data to interested parties or provide additional services to customers based on the data.
Using data to improve your business strategy and look for new commercial opportunities requires a bit of a mindset shift for most companies. This chapter is about building a culture of data-based decision making in your business and viewing data as an ongoing commitment to improvement, at all levels in the company.
Many business owners and managers make decisions based on gut feeling rather than hard facts. Sometimes this works out; sometimes it doesn’t. The truth is, solid facts are far more likely to lead to consistently good business decisions – and that’s where data can help.
In an age when everything can be measured, quantified and analysed to gain new insights, it makes sense to use that process to improve your decision making. Basing decisions on what data tells you helps you to implement your business strategy faster and more efficiently – whether you want to increase staff retention, cut wastage by ten per cent, increase efficiency in your manufacturing process or achieve some other meaningful objective.
There are two key aspects of building a culture of data-based decision making:
Your ultimate aim should be to create a culture in which people naturally want to make better decisions and look to data to help them do so.
A good way to sow the seeds for improved decision making is to engage key personnel in developing your data strategy, which I talk about in Chapter 10. For example, if you want to use data to better understand and target your customers, then involve your marketing head from the outset. Or, if you want to improve manufacturing output and reduce unexpected downtime, then you’ll need to get your manufacturing people on board. Encourage those key personnel to become data advocates, creating a trickle-down effect.
Following these pointers can help everyone in the business leverage data and move towards more fact-based decision making.
There’s no denying it: change is difficult for many people and businesses. And implementing a cultural shift in an organisation, whether big or small, is not a quick and easy job. But it is crucial if your company is to make smarter decisions going forward. Focusing on the positive outcomes certainly helps smooth the way.
Broadcast positive goals and outcomes load and clear. If data has helped reduce wastage by ten per cent, then you should be shouting about that success. If you’re only at the beginning of your data journey, share examples from similar companies. Seeing how other people have changed their decision-making processes can provide a helpful nudge in the right direction.
Even after you answer your strategic questions (see Chapter 11), the fun doesn’t stop there (or it doesn’t have to). Data can help support ongoing business practices, challenge how you do things in your company, and influence all areas of your business strategy.
You can use data in your business in literally millions of ways, but here I focus on four key examples: managing talent, boosting employee satisfaction, increasing operational efficiency and optimising business processes. These examples give you an idea of how data can help improve decision making and change your organisational culture for the better.
Big data is now filtering through into human resource processes. Largely, this falls into two camps: finding the best talent and hanging on to that talent!
Recruitment has traditionally been a somewhat hit-and-miss affair; statistics show that almost half of appointments end up as failures within 18 months. That shouldn’t be surprising; personalities are often the hardest things to accurately convey and interpret in an interview and around 90 per cent of appointment failures are down to attitudinal reasons.
Perhaps more surprising is that companies continue to make hiring decisions (with all the financial liabilities that they entail) based on an often brief interview and a cursory look at a CV. Much of the time the decision is based on an interviewer’s gut feeling. Considering that a large proportion (40 per cent to 60 per cent by most estimates) of a company’s revenue goes on staff salaries, it makes sense to be a bit more strategic about the recruitment process and base decisions on data rather than instinct.
Many companies are already using Google, Facebook and Twitter to search a potential new hire’s name to make sure there’s nothing lurking in the woodwork.
Services are emerging that allow employers to assess a candidate’s suitability using more subtle indicators. As well as semantic analysis of the language they use, patterns of behaviour (such as enthusiasm for subjects related to the job) can be monitored and compared against patterns shown by previously successful employees.
Tools such as Cornerstone (www.cornerstoneondemand.com
) and TalentBin (www.talentbin.com
) allow employers to crunch data in more ways than ever to find the right candidate for the right position. Some companies have taken it even further. For example, hotel chain Marriott created its own Farmville-style game that simulated the running of a hotel to test the abilities of potential candidates. The experiment was reportedly not a huge success (the game was apparently rather boring), but it demonstrates new ways of thinking that companies are applying to decision making.
Another often-cited example is that of office equipment manufacturer Xerox. An analytics firm was asked to monitor staff performance and then come up with a profile of an ideal candidate for its call centres. Among the surprising findings was that previous call centre experience was no indicator of success, and that candidates with criminal records often performed better than those without. The experiment led to a 20 per cent reduction in staff turnover.
Most employers still rely on gut instinct to some degree – and that’s not necessarily a bad thing. The best decisions are those that are informed by data but interpreted through experience and common sense. There will always be a need for the human touch when it comes to recruiting. But, as in many other areas of business, data is certainly helping to take the guess work out of recruitment.
Companies now have more data than ever on their employees and more tools and technology with which to analyse this data. As well as optimising talent acquisition, big data tools can help companies measure and improve company culture, staff engagement and overall employee satisfaction.
Implications are also beginning to be realised for health, safety and well-being at work. Obviously, when people become ill at work or are absent for long periods of time, it’s stressful for the individual involved, but it’s also detrimental to the business and the people left picking up the extra workload. Personal analytics and health monitoring can change all that and give you a real-time insight into health and well-being. Hitachi’s Business Microscope service enables companies to fit its staff with Radio Frequency Identification (RFID) tags that track their movements around the workplace and even monitor sound waves to identify how stressed or relaxed they are when they speak.
People who work in potentially dangerous or stressful environments are being measured to monitor their fatigue and stress levels so that employers can pull them off jobs before they get too tired and potentially cause an accident.
Data can provide a wealth of insights in terms of productivity, and even sales. In one trial, a retailer was able to increase sales by 15 per cent after it noticed that the presence of a member of staff in certain areas of the store had a high impact on products sold, while in other areas, staff presence had very little effect. In a seated office environment, technology can record how long employees spend at their desks, how much time they spend interacting with other staff, whom they talk to, the distance they stand from each other during conversations and the enthusiasm with which they contribute to meetings.
Operational efficiency can be defined as delivering products or services in the most efficient and cost-effective way possible. Often operational decisions are based on experience or ‘the way things have always been done’. But data and technology have a lot to offer in terms of making your business as efficient as possible.
For a business, the ability to have its production, stock control, distribution and security systems all connected and talking to each other means greater efficiency and less waste.
Big data analytics also help machines and devices become smarter and more autonomous (for more on sensor data, machine data and the Internet of Things, see Chapter 5). Manufacturers are monitoring minute vibration data from their equipment, which changes slightly as it wears down, to predict the optimal time to replace or maintain parts. Doing this too soon wastes money; doing it too late can trigger a failure and expensive work stoppage.
Ideally, your people will look to data to improve all aspects of business decision making in all areas of the company. This includes everything from stock management to customer relationships to security.
Many retail companies are already using algorithms to understand what’s trending in social media and what competitors are charging in real time. Algorithms are also great for recommending other products a customer might like – a strategy basically pioneered by Amazon and used to great effect.
I have worked with a number of hotel chains that want to move away from the traditional in-house surveys, which are costly and questionably accurate, to using social media to analyse what people are saying and posting about the hotel. This way, hotel managers can better understand their customers and improve their service. By running and using sentiment analysis (see Chapter 5) on Facebook posts, tweets and other social media sites and reviews on Trip Advisor, in addition to existing data, hotels are getting far more reliable information than they would from a survey.
Stock management is another area with enormous potential, using data from social media, web search trends and weather data, for example, to build predictive models for what your customers will want and when. One often-cited example is the supermarket chain Walmart that discovered a surprising correlation between hurricane warnings and sales of Pop-Tarts. Apparently, in a hurricane, people just want to hunker down and eat Pop-Tarts. Perhaps the UK equivalent is huge sales of charcoal, sausages and hot buns on the first vaguely warm day of the year – usually in April and usually when the temperature is only around 15 degrees Celsius (around 60 degrees Fahrenheit)!
One particular business process seeing a lot of big data analytics is supply chain or delivery route optimisation. Here, GPS and radio frequency identification sensors are used to track goods or delivery vehicles and optimise routes by integrating live traffic data. For instance, if a delivery driver has an optimised delivery schedule, that schedule interacts in real time with weather data and traffic data so that if there is a traffic jam, accident or reports of delivery-impacting weather such as snow or storms, the schedule automatically calibrates an alternate route. Optimising this business process can also include delivery companies putting sensors on pallets and handheld devices that record delivery and monitor where drivers are, while also monitoring the engines of the delivery vehicles to create dynamic servicing schedules.
Technology can also improve business security and reduce fraud, with the potential for huge financial savings. In fact, big data is already applied heavily in improving business security through CCTV (closed-circuit television) video footage analytics. Credit card and insurance companies are already using data to prevent fraud. Insurance companies, for example, are using big data algorithms to check for fraudulent claims as well as anomalies in policy applications. Algorithms can now take into account the speed at which you complete a claim or application form – to spot those completed by machines versus people – as well as whether applicants have gone back and changed their initial application to reduce premiums by maybe not admitting a recent claim or decreasing their annual mileage.
One way that big data can transform your business is by helping to identify new or additional business models. Thanks to the massive explosion in data available and our increasing ability to analyse that data, some companies have radically changed their commercial models and moved into new territory. For some, data is changing the very nature of their business. For an example of a company that’s based its entire business model on big data, check out the sidebar ‘How Uber uses big data - A practical case study’.
Applying data to improving existing operations should be your primary focus, and this is where your strategic questions come into play (I discuss them in Chapter 11). However, once you have identified and answered your strategic questions, you may find that the data also points to interesting new opportunities – something that may take your business in a new direction.
What opportunities exist depend very much on your company and industry, but here are a few quick tips to help you identify opportunities for new business models in your data:
Ultimately, access to data and the ability to analyse it allows you to review evidence and make better decisions based on fact, not assumption or gut feeling. With data at the heart of everything your company does and every key decision you make, you’re in a position to apply insights for the better across the board.
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