This can be an existential threat to businesses, but it also offers a wealth of opportunities. Transitioning to a digital, data-driven model can enable companies to experience renewed growth and better position themselves to face the future.
Data is critical when we look to make these transformations. Today, businesses have access to more information than ever before, as well as increasingly sophisticated technological tools like artificial intelligence (AI) with which to make sense of it.
In this post, I will look at some of the ways that data can be used as a catalyst for positive innovation and transformation. This includes ensuring products and services remain aligned with customer needs and behaviors, streamlining business operations, and creating more powerful customer experiences.
What are modern business models?
Firstly let’s take a look at some of the business models that are successful in today’s technology-driven markets. Lots of businesses operate by combining two or more of these models to come up with a unique set of offerings.
E-Commerce businesses – These are businesses that sell products and services online. Some are digital natives, like Amazon, and some switched to e-commerce models or adopted them to augment “traditional” retail models, such as Walmart and Tesco.
Ad-Supported Businesses – Media businesses like television networks and magazine publishers traditionally generated revenue from advertisers. Today, a wide spectrum of businesses, from social networks like Facebook to software services providers like Google, generate revenue by displaying advertising to their users.
Subscription Services, from Netflix and Spotify to health and wellness services like Fitbit, home delivery grocery businesses like Hello Fresh, insurance and financial services, and car manufacturers - just about anything can be bought on a subscription basis now. Customers no longer "own" the products but can upgrade to the latest versions whenever they are available and don’t have to deal with obsolete products cluttering up their lives.
Marketplaces – These are services like Amazon, eBay or Alibaba that let businesses set up shops on their platform in exchange for a fee or a slice of their revenue.
Aggregators – These are services – usually websites – that scrape the web for products and services in a particular niche – from insurance services (comparethemarket) to retailers (Pricerunner, shopping.com) or vacations (Expedia) and compile them in a handy location for shoppers to browse. They generate revenue by taking a commission from the provider when we use them to make a purchase.
How Data Transforms Businesses
Data is a powerful driver of innovation. One of the many ways it can be used is to create products and services that are more in line with customer needs.
Both internal and external data can also be used to determine behavioral and demographic trends that mean a company might need to rethink its core offerings.
It can also be used to optimize business processes in order to streamline operations and minimize waste. For example:
· Shipping and inventory data can be used to ensure that logistics and warehousing operations are working at optimal capacity, without wasted space or inefficient deliveries causing unnecessary expense.
· Human resource processes such as hiring, training, and monitoring the productivity of staff can be augmented with automation technology to become more streamlined.
· Marketing departments can ensure their messaging is on-target and ad spends are managed effectively.
· And finance departments can automate routine work like bookkeeping and payroll, as well as more accurately model business operations and market conditions.
Using Data to Develop New Business Models
Over the past decade, one of the most successful business model changes has involved companies that traditionally provided products switching to a services model.
As well as manufacturing tractors and farming machinery, for example, agricultural manufacturer John Deere now provides data services that enable farmers to more accurately predict crop yields, manage the maintenance of their equipment, and reduce the volume of chemical fertilizers and pesticides they need to use.
Likewise, electronic equipment manufacturer Philips created its Connected Healthsuite digital platform to bring together data from all of its professional medical devices used in hospitals in order to give patients and caregivers access to data that can be used to improve wellbeing and patient outcomes.
And rather than simply manufacturing industrial machinery and engines, General Electric fitted sensors to its products to enable it to offer data insights through its Predix platform.
The number of connected devices in our homes and businesses is increasing exponentially, and many of these act as sensors collecting data that tells us about the world and how we are living. Companies that create these devices can then offer new services based on how we use them.
This could be more efficient energy tariffs depending on how we use the devices in our homes. And Manufacturers of smart camera and security systems have pivoted to become subscription services that intelligently monitor our homes.
Another example is automobile manufacturers packing vehicles with sensors and cameras that monitor how they are driven. This data is being used to develop autonomous vehicles that can more accurately predict and respond to the behavior of human-piloted vehicles they share the road with.
Data is also increasingly being used to offer improved customer experience and personalization. This helps companies to stand out from the competition in a world where customers increasingly value the experience provided by a brand alongside traditional differentiators such as price or quality.
E-commerce giants like Amazon, Facebook and Google built their brands on the back of personalized experiences, including recommendations and personalized marketing strategies. Similarly streaming services like Netflix and Spotify redefined the way we consume entertainment media with personalized schedules and playlists.
Key Challenges of Data-Driven Business Model Transformation
Today, getting hold of data is rarely the problem. As we’ve covered, it’s everywhere, and the tools for capturing it and storing it are increasingly in reach even for smaller businesses.
Thanks to the proliferation of Internet of Things (IoT) devices, scanners, cameras, and sensors, along with falling hardware prices and cloud storage infrastructure, Big Data analytics and AI is no longer reserved for large organizations with unlimited IT budgets.
The challenges that businesses are likely to come up against are more likely to fall into one of the following categories:
Aligning data strategy with business strategy:
Setting out to capture and analyze everything with the hope that it will provide insights that guide you toward business model transformation is unlikely to be the best course of action. Instead, businesses should have clear ideas about what they are trying to achieve – with reference to some of the points covered above.
Are your products and services no longer meeting the needs of your customers (or are those of your competition doing it more effectively?)
Do you want to improve the experience of your customers as they interact with your marketing, products and services, and support infrastructure?
Do you need to reduce the expenses incurred and the waste generated as you carry out your core business activities?
Identifying questions that need to be answered here can often prove to be the key when it comes to opportunities for business model transformation.
Building a Data-Driven Culture
Business leaders can anticipate that they may come up against some resistance when attempting far-reaching changes like business model transformation.
Often there will be an attitude of “if it ain’t broke, don’t fix it” – fine in principle, but ignoring the fact that a breakage could just be one innovative competitor, or missed opportunity, around the corner!
There could also be antipathy from leadership and management that are used to getting by on instinct or trusting their gut feeling, or a workforce worried new technology could be a hindrance rather than a help – or could even make them redundant.
Successfully navigating business model transformation is likely to require ensuring buy-in at all levels of the organization, as well as ensuring you have the right people in place with the necessary skills to make it happen.
Data Security, Privacy and Governance Issues
Businesses that work with data – particularly personal data, which tends to be the most valuable type – have to follow stringent rules and regulations on how it can be used. These include the European GDPR and the Californian CCPA. More states and nations are working on passing their own regulations around the use of personal data in the near future.
In addition to this, cybercrime and data breaches are an increasing concern for any business that stores and works with personal data. Failing to take adequate safety precautions and implement a robust cybersecurity strategy can leave companies facing potentially ruinous fines and sanctions.
Implementing strict controls over who has access to what data, use of end-to-end encryption tools for all data transmissions, and ensuring that regular security audits take place are all critical security steps that must be in place when planning data-driven transformation.