Tech Trends in Practice. Бернард Марр
Читать онлайн книгу.
boosting productivity, strengthening the supply chain – across all areas of the business, it’s easier than ever to make improvements, generate efficiencies, save money, automate processes, and more.
Remember the Otto example of predicting demand in order to improve stock ordering? Thanks to data (and more than a bit of AI, see Trend 1), this impressive process happens automatically. The company’s system orders around 200,000 products a month without human intervention.
In another example, Bank of America worked with Humanyze (formerly Sociometric Solutions) to implement smart employee name badges, fitted with sensors that can detect social dynamics in the workplace. From the data generated, the bank noticed that top-performing employees at call centers were those who took breaks together. As a result, it instituted new group break policies and performance improved 23%.7 You can find more examples of enhanced and automated business processes in Trend 13 (robots and cobots).
Creating Additional Revenue
Optimizing business processes, making better business decisions, and so on, will no doubt have a positive impact on the bottom line. But the link between data and the bottom line can be much more explicit, meaning data can be monetized to create new revenue streams.
This may encompass bringing new data-driven products to market (such as the smart products outlined in Trends 2 and 3), or it could mean actively selling data through optimized services (such as Google’s data-driven advertising offering). Data can even increase the value of a company; at the time of writing, the world’s top three most valuable brands were Google, Apple, and Amazon – each of them data-driven businesses.8
Key Challenges
You might think that some of the most obvious challenges around big data are the technology, infrastructure, and skills challenges. To put it another way, do you have to have the budget, infrastructure, and know-how of, say, Google or Amazon to benefit from big data? Thanks to augmented analytics and big-data-as-a-service (BDaaS), the answer is no. I’ve covered augmented analytics earlier in the chapter, so let’s briefly look at BDaaS. The term refers to the delivery of big data tools and technology – and potentially even data itself – through software-as-a-service platforms. These services allow companies to access big data tools without the need for expensive infrastructure investments (see also AI-as-a-service in Trend 1), thereby helping to make big data accessible to even small businesses. This also helps to overcome the massive skills gap in big data. Essentially, there aren’t enough data scientists to go around; the McKinsey Global Institute predicts that, by 2024, there’ll be a shortage of approximately 250,000 data scientists – and that’s just in the US.9
As analytics tools advance, my hope is that technology, infrastructure, and skills will become less daunting barriers to working with data. But that doesn’t mean there won’t be other barriers to contend with. I believe two of the biggest challenges around big data are data security and privacy.
Ever-growing volumes of data – and the fact that data is becoming more of a critical business asset – brings huge challenges in terms of protecting that data. It’s therefore vital that organizations take steps to protect their data from attack, particularly when it comes to personal data (like customer or employee data). Advances like the IoT add an extra dimension to the threat, since many connected devices are totally unsecured, thereby providing a potential way in for hackers. (One study has found that 82% of organizations believe that unsecured IoT devices will cause a data breach in the next few years.10) But your employees are another significant threat to consider. So as well as having a robust data security policy in place, it’s vital you raise awareness of the potential threats and educate your teams on the need to protect data.
Security is closely linked to data privacy, since so much of the data that organizations are working with contains personally identifiable information. Regulators are, to some extent, still playing catchup when it comes to data privacy laws, but that will change. Recent GDPR guidance in Europe is designed to promote the safe and ethical handling of personal data – and give individuals a greater say in how organizations use their data. Therefore, it’s not enough to protect your data securely – you also need to take an ethical approach to collecting and using that data. This means being completely transparent, making customers and other stakeholders aware of what data you’re gathering and why, and giving them the chance to opt out where possible. Those companies who don’t comply with tightening regulation, or who play fast and loose with people’s data, risk serious financial and reputational blowback in the future.
How to Prepare for This Trend
Despite the challenges, most experts, myself included, believe the benefits of big data are huge. Data can bring enormous value to your organization, providing you prepare properly. For me, this means:
Improving data literacy across the organization
Creating a data strategy
Let’s look at each step in turn.
Improving Data Literacy Across the Organization
The more data literate your organization is, the better your results will be. It’s as simple as that. But that doesn’t mean everyone has to be a data scientist. It simply means that everyone right across the business must be comfortable with data: talking about data, using data, thinking critically about data, pulling meaningful insights from data, and ultimately acting on what data tells them. Data literacy is about everyone putting data to use, essentially.
Raising data literacy across the business is a case of establishing your current levels of data literacy, communicating why data literacy is important, identifying data advocates who can sing the praises of data, ensuring access to data, and educating those across the business on how to get the most out of data.
Creating a Data Strategy
It’s also vital you have a data strategy in place. A data strategy helps you remain focused on the data that matters most to your business – as opposed to collecting data on anything and everything, which is rather an expensive way to go about it! With so much data available these days, the trick is to focus on finding the exact, specific pieces of data that will best benefit your organization. A data strategy helps you do just that. With a robust data strategy you can set out how you want to use data in practice, clarify your top data priorities, and chart a clear course to achieving your goals.
Your data strategy must be unique to your business, but, broadly speaking, I’d expect a good data strategy to cover the following points:
Business needs. To truly add value, data must be driven by specific business needs, which means your data strategy must be driven by your overarching business strategy. Basically, what is your business trying to achieve, and how can data help you achieve those strategic objectives? Here, it’s wise to identify no more than three to five key ways in which data can help the business achieve its strategic goals, answer key business questions, or overcome its main challenges. Then, for each data use, you then identify the following…
Data requirements. What data do you need to achieve your goals and where will that data come from? Do you, for example, already have the data you need? Do you need to supplement internal company data with externally available data (such as social media data)? If you need to collect new data, how will you go about that?
Data governance. This is what stops your data becoming a serious liability, and involves considerations such as data quality, data security, privacy, ethics, and transparency. For example, who is responsible for making sure your data is accurate, complete, and up to date? What permissions do you need to secure in order to gather and use the data?
Technology requirements. In very simple terms, this means looking at your hardware and software needs for collecting