The Digital Transformation of business can be overwhelming. Unless you’re a start-up, social networking, predictive analytics, the Internet of Things, blockchains, etc., can easily bring you to a state of information congestion and a strong sense of inadequacy. Thus, I thought I’d give it a shot at trivializing the topic and help make the entire subject more digestible. Thus this article.
Digital is indeed powerful, far reaching and inevitable. Think of it this way: in the 20th century, the heartbeat of the industrial revolution was the machine (engine) and Oil. It was the century of big things and large amounts of capital concentrated in big corporations. This century, the heartbeat of the economy is Digital – the combination of software apps, connectivity and data. This triangle has grown very robust, has been commoditized, is inexpensive and therefore accessible and is giving birth to new business models and a vast wave of disruption in all industries.
Amazon, GE, Braintree, Stripe, Starling, AirBnB, Uber, Netflix, Skype, Facebook, Snapchat, Olo, Deliveroo – are all examples of the power of Digital Disruption. But if it’s so pervasive it’s also because it’s not that complicated, it’s within reach of everyone and just requires a bit of imagination… and – alas – a great deal of open mindedness. Not complicated but not easy either.
Let me try to simplify this, then.
First, let’s talk about an important principle: to think of “Digital” simply as a technology is a big mistake. If you took all these technologies – social tools, analytical tools, machine learning, Internet of Things, Fintech – and implemented them all in your Enterprise but changed nothing else, nothing would happen. You would do the same things with new tools and that’s all. In time, many of those tools would be abandoned.
Digital means transformation, it means moving to a new place. If you can’t accept that, don’t even start down this road. It’s really important that you come to terms with this simple and yet stark reality – Digital is a bridge to a new universe where your business won’t look the same. It’s a bridge that everyone has to cross or face the inevitable faith of being overtaken by start-ups with great imagination, free from old paradigms and armed with powerful and yet inexpensive technologies. Uber, Netflix, Amazon…
Digital Transformation hits the Enterprise at 3 levels: Culture, Strategy and Technology. Let’s look at each of these.
The “Digital bridge” to a new place starts with Culture. Surveys show that Digitally Mature companies have common cultural characteristics that must be fostered and developed to achieve the opportunities of the Digital era. I.e., Digital is not just a structural thing; in my mind, today’s disruptive trends are so fuelled by Start-Ups that in order to join the wave and compete, you need to think like a Start-Up. Hence, the focus of cultural transformation needs to be Speed and Innovation.
My best advice to help you move towards cultural change in the right direction, is to adopt social models. A Social Model means that you move from being organized in directive Functional Hierarchies to a model of Communities of focused objectives.
A Community, in this sense, is a group of people of multiple skills working collectively towards a single objective, which they are responsible for as a Group. You can read more about it here (I refer to Communities as HIVE’s) but one of the great attributes of Communities is that they are extremely fast in problem resolution. This element of Speed is accompanied by openness, mutual trust and intimate collaboration. Openness and collaboration, in turn, foster an environment of risk taking (risk sharing), rapid experimentation and open innovation. It enables Enterprise communities to collaborate with communities from other Enterprises, leading to co-innovation with Suppliers and with Customers.
Rapid experimentation, open innovation, embracing change – these are the key cultural attributes of Digitally Mature companies and, in my view, the best way to achieve them is to move to collaborative Communities.
Olo is a company that bridges consumers with Restaurants and manages ordering of items in Restaurants’ menus directly from consumers’ homes. But Olo isn’t just a switchboard of sorts or a ‘universal catalogue’. Olo uses powerful Analytics algorithms to predict where Demand comes from, areas of the city where customers will be ordering, to help Restaurants plan fresh food stocks and, down the line, where their kitchens should be placed.
Uber does something similar with the management of fleets of drivers – where they should be, given where Demand will come from.
AirBnb, Uber and Olo have a few things in common: first, they exploited a need that was not being fulfilled adequately and capacity that either wasn’t being used or could be exploited differently; second, they make use of powerful Predictive Analytics to make their business models more powerful. And thus, with a simple App and access to powerful Cloud services they are upending their respective industries.
Digital Strategy doesn’t just mean that you undertake projects to implement Digital technologies. With the right culture, Digital Strategy means that you craft your company’s business strategy taking into account Digital tech and then you deliberately disrupt your business and possibly your industry. Don’t focus on implementing a Digital footprint; focus on finding and implementing a disruptive Digitally-fuelled business model. You can take a look at a brilliant write up by McKinsey here as a reference on how to reinvent your business strategy in these terms.
Ah! Finally I get to the ‘juicy’ part, huh? Well, don’t forget what I said earlier: if you just do Tech, you won’t get anywhere.
Digital Tech is also the hardest part to come to terms with and what is most overwhelming. It is easy to get lost amidst all the forms that Digital takes but it can be summarized as follows: “Digital” means primarily three things: social tools; Big Data and AI; and Mobile Apps.
Social Tools are those that we use to enable Communities to communicate virtually. They have two primary forms: Instant Messaging (like the texting you do with your smartphone) and Conversations – done with so called Enterprise Social Networking or ESN tools. Examples of the latter are Yammer and Jive.
Big Data and AI is where things get more confusing but they don’t need to be. In Analytics we need to think of the following:
- Big Data means lots of data and data of various forms – structured, unstructured, numbers, text, voice, video and data from sensors. But the term implies that we go crazy on data, we get all that we can get our hands on, the more the merrier, the greater the diversity the better. We will use new storage technology provided by 3rd parties through the Cloud to spoil ourselves with large volumes of the stuff.
- AI means a few things:
- Visualization: the presentation of synthesis of data in graphic form to surface insights more easily than through tabular reports.
- Predictive Analytics or Machine Learning – this is where the value of Analytics and Big Data comes together. PA refers to predictive models that predict a future event based on past data. The power of the technology is machine learning, where these models aren’t designed by you, they are designed by algorithms that ‘learn’ from the data. The more and more diverse data there is, the more the algorithm can learn and the better the predictive model it generates.
- Deep Learning – similar to Maching Learning, Deep Learning uses more advanced algorithms and predictive models. Deep Learning builds neural networks
AI has wide and far reaching applications, from fraud detection to law enforcement to health care, financial services, customer management, manufacturing productivity, etc. But at a very practical level, it allows us to make business decisions based on data – on insights drawn from data – as opposed to relying solely on instinct and talent, using enormous amounts of data processed at great speed.
In Industry – supply chains, manufacturing, logistics – we connect physical equipment to Big Data storage and AI services through what is called the Industrial Internet of Things or IIoT. Manufacturing equipment, for instance, usually comes with sensors and a computer that draws data from those sensors and shows information about machine state and performance. At the end of the day, this data is lost. With IIoT, these computers and sensors are connected through the Internet to a Cloud service (e.g. IBM, Microsoft, GE Digital are companies that provide IIoT and AI services) where the power of AI can be leveraged to monitor machine equipment performance and determine where to optimize that performance.
AI is widely used on the Customer front-end where data resulting from interactions with Customers – could be anything from Salesforce.com data to emails to conversations in social networks to data from open data sources – is used, through AI, to detect customer behaviour and optimize services and ways of retaining customer loyalty.
Big Data, AI and IIoT connectivity are all very complex subjects, routed in Data Science. Fortunately, this complexity can be shielded by service providers as mentioned earlier. You don’t need to invest heavily in IT to develop these capabilities in house. You can use 3rd party Cloud services and then focus only on what using AI means to business.
Finally, Mobile Apps. The power of information, the visualization of Analytics and the insights of AI are best accessed through Mobile Apps to make better decisions and provide better customer service. Mobile Apps allow organizations to become mobile, thus liberating employees from the desktop and the cubicle and evening the capabilities across stationary as well as mobile staff. Mobile Apps don’t have to be developed in house since there are good 3rd party services to help with tablet-based Apps. IBM, as an example, has done a tremendous job building Enterprise Business applications for the iPad and, in addition, providing an iOS App development platform that is extremely powerful and accessible, with the great advantage that AI is built into the architecture of the platform.
The Digital challenge is one of disrupting old paradigms and migrating to a new world of great speed and agility.
First you must develop a culture of open collaboration, employee empowerment, rapid experimentation and risk taking. You can accomplish this by adopting social models of communities in the workplace.
You must work on rethinking your business strategy in terms of disrupting your own business model through Digital means. But without the right culture, you won’t get there.
Lastly, Digital Tech can be complex but it really boils down to Social Tools, Big Data and AI, and Mobile Apps.
Big Data and AI bring the power of Predictive Models. This Tech can be accessed through 3rd Party services and in the Materials world of Manufacturing and Logistics, equipment can be blanketed with sensors and the latter connected to a Cloud-based IIoT platform to leverage the power of AI in monitoring and improving the performance of the supply chain.
As I said: it requires a bit of imagination and a great deal of open-mindedness. Technology is complex but it’s really the easy part because it is so accessible.