Data analytics is changing our world in unprecedented ways, driving new products, business models, and breakthroughs in every industry. Machine learning helps companies act strategically on the data they have, homing in on the insights with impact, and allows executives to make much better-informed decisions. In a recent study, it was shown that companies were 26 percent more profitable than their industry competitors, generated 9 percent more revenue through their employees and physical assets, and enjoyed 12 percent higher market valuation ratios when using this type of data-centric approach.
However, data at its core is not organized or clean, and it certainly doesn’t come already structured in a database. It’s a messy swamp of details of what we buy, where we drive, what we surf online, and what we “like”. It’s transactions, chats on social networks, cellphone conversations, tweets, texts, photos, videos, your web search, and browsing patterns. Every minute of every day, data is being generated, by each of us.
A hyper-focused data science approach can give companies of any size a competitive advantage, but unfortunately, data alone has no intrinsic value. Sense needs to be made of it before uncovering the magic behind the curtain. Simply knowing that is the first step, but taking that first step can be confusing.
Building this foundation will allow you to have an environment where data-centric decisions can be made, but do not start a data science project unless you know why you’re doing it and what it should look like when it succeeds. Think very hard about the goals. Are you looking to increase conversion rates? Marketing ROI? Market Share? Customer Lifetime Value? Measure where you are now, where you think you could be, and how much revenue that translates into. As a wise man once said, “if you can’t measure it, you can’t manage it”
Unfortunately, due to the business lifecycle of most companies, management expects an almost immediate return on new initiatives. With AI/ML, before a company can optimize its business or build data products more intelligently, the infrastructure needs to be developed. One of the recipes for disaster is for startups to engage its first data science contributor who only specializes in cutting-edge modeling, but has little experience in building the strong initial foundation that is the prerequisite of everything. The messy swamp of data needs to be collected and made into a nice, clean lake. Only then can patterns, insights, and predictions begin.
As with most things that are worth doing, making a data science program effective can take substantial effort and (like everything in tech) will require several iterations before the AI/ML is structured effectively. Don’t give up
Imagine your company’s data journey as one climb where on top you have true power and predictions that will separate you from the competition and give you an advantage. This climb is a slippery slope full of challenges but when you do it right, your data insights team will have an easy time coming up with ideas and testing them in production systems.
Usually, clients read blogs on AI/ML adoption topics and they come coming with various requests without first questioning the state of their infrastructure. Is your data stored in a centralized place? Can you pull it out easily? Can you visualize it easily? Can you explain trends, anomalies, and causality?
The majority of companies think they are already on top and just need to apply the same solutions as the competition and their business will flourish. However, the story is a bit different. Data is all over the place, it is not well structured, important data points are missing, the data science team cannot access data easily, there is no possibility to visualize data nor to test the hypothesis, and infrastructure is lacking the possibility to apply simple A/B testing.
The Data journey should start at the bottom in the Collect phase. Most systems are old and were built before this information revolution, and they are not capable of storing huge amounts of data. On the other hand, startups are rushing to build something presentable and are choosing common tools (relational databases) that cannot cope with these amounts of data. Forward-thinking companies are deciding to move to the cloud (because of the elasticity of expanding data infrastructure) and build data lakes, where data will be stored in a centralized place and easily accessible for data science teams. Easy access and all the important data points stored in one place is a major prerequisite for the Analyze phase which unlocks the true potential of your data.
The Analyze phase is interesting, it is bringing technical and business people to the same table. The first step is to talk about pain points, what prevents businesses from doing their job most efficiently? Do you struggle with analytics? Do you know how many resources to put into some task to most efficiently do the task? Where do you plan to be in the next quarter money-wise? How will you prevent your customers from churning to the competition? We have created business insights as a service for this phase specifically. We need two things here, the pain points of business owners and a portion of data to analyze. What we come up with after this workshop is an idea document that will list potential projects that can ease up business pain points and are viable, based on the data analysis we perform. Also, we concentrate on ROI since estimates and timelines are given, so you can calculate investment and compare it to potential ROI.
You are almost there, one step from the top in the Predict phase, where we leverage the true power of the data. We have project ideas with ROI analysis and can decide which is the best to tackle first. Pick one that sounds best from an ROI perspective, and make sure you have all roles in your team. A Data Scientist is just a small part of the team-building algorithm for the project, you need Data Engineers to choose the right tools for the job, as well as infrastructure engineers familiar with data problems to integrate this new project with existing infrastructure.
Last but not least is the AI phase, fundamentals and infrastructure are there and now it is time to add the brain to your operations. This phase is all about self-learning systems that are becoming clever over time, human in the human-in-the-loop approach, and retraining here is a must, reinforcement learning is a good trick in how systems can become smarter over time. Here strategy meets implementation, you need to think about the long tail effect and how small tools and aids can make the system clever over time.
The Data-Driven Business Healthcheck
Our Data Driven Business health check is a short list of questions that you should consider as the minimum framework of needs and covers the basic mindset of anyone thinking about Deep Learning and AI. It helps formulate the answer to the simple question of: Are you Data Data-driven business?
Having the right architecture and foundation for your AI and data analytics functions is as important as having bricks and mortar for a physical office. Why, you ask? Not surprisingly, data scientists and data analysts need data. Many techniques require a minimum of tens of thousands if not hundreds of thousands or even millions of data points to build.
If you are a startup and you have not launched yet, you do not need a full-time data scientist. Without a basic understanding of what drives the organization, it becomes very difficult to make use of modeling techniques. For example, a data scientist can use Machine Learning to make predictions like which users will churn or become highly active, however, if you don’t have a definition for these, you are setting up the DS program for failure. It’s difficult to validate them if you don’t have sufficient metrics with which to evaluate.
Check out our Data Driven Business Healthcheck on this link and let us know what you think.
There is no need to be an expert in AI and data analytics to hire the right kind of talent, however, you should have a good idea of what is and isn’t possible so that you don’t set unrealistic expectations. Data science isn’t magic and it’s not even a traditional science. It’s just as much an art as it is a science, which means the variability in skills and ability is substantial. Don’t expect magic from your data strategy on day 1.
If you go for data scientist without first realizing where you are in terms of AI readiness you will be surprised and find soon enough that your data scientist cannot work efficiently with data. He will complain that the infrastructure is not ready, he does not have all the information, he cannot test his hypothesis, and he cannot pull and visualize data.
The second most important thing is not to use data science for the sake of data science. Read what your competition is doing, listen to the market, and try to pick up on things you think will work for your business. Create a clear data strategy and define goals so you can evaluate investments in data science. Then decide whether you want to build your data team slowly or you want to kick-start an internal data team with the help of a company that has already implemented a couple of projects.
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