Data is important. While I don’t need to convince you on the importance of good information, I often find myself in the position to convince businesses to do more with theirs. In fact, data should be the most important thing on the agenda. That may be more of a stretch for most. After the Scientific Revolution, knowledge was defined by our ability to apply mathematics to empirical data. Intelligence, by definition, takes things a bit further:
Intelligence is the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.
So intelligence, by definition, is all about how we calculate data. The difference today is that we have exponentially more access data and incredible machine learning algorithms to help us understand, predict, and act in an advanced way. Companies are getting ahead by collecting data exhaust, applying intelligence, and taking the correct actions. Accenture, Keller Williams, Meltwater, and KUNGFU.AI are a few who are leading by example. At SXSW 2019 these companies hosted a panel to share just how they turn data exhaust into advantage. Let’s overview a few key points.
There is a lot of data exhaust out there.
By the end of 2020 over 44 zettabytes of data will be available. To put things in perspective, that is roughly equivalent of 5 billion libraries of congress or 4 years worth of hi-def video. Having enough data is not an issue. Data is all around us. It’s everywhere and being produced at an exhausting rate. The issue is that most of this data is not being harnessed or is completely dark.
Treasure troves of data is either being lost in the wind or is collected but not being used in any meaningful way. The opportunity is data exhaust. Data exhaust is a collection of information, facts, micro or macro interactions that are captured but not utilized as intelligence. Businesses today are improving their internal and external capabilities with how they harness data exhaust.
How do you capture data exhaust?
A safe bet is to start capturing everything. For example, Keller Williams decided to harness 30 years of offer contract data to analyze winning and losing offers to make predictions on the success of future offers. Also have a plan to own data relevant to where the business is focused. Keller Williams decided that equipping their brokers with differentiated intelligence was important to maintain an advantage. Begin by building your data strategy. Every business needs to identify what data is meaningful to them and how to go get it. When considering what data to target, think in two broad classes; both internal and external data. Internal data may include memos, emails, service reports, call center logs, sales performance, customer profiles, website analytics, on and on. When it comes to internal data, identify the data you have in large quantities but also consider other important data in low supply. External data is where things get interesting. The external data comes from data suppliers, social media, news, or the web. For example, leveraging social media data can help companies understand consumer behavior and market intelligence that informs how you find new customers. You can partner to source B2B financial data, business profiles, or even location/geospatial data. You can use external data to help fill data gaps. When you enrich internal data with external data, the intelligence becomes much more robust.
You need to consider how to get AI ready.
To build a comprehensive data strategy or to implement AI, you need support from the top. An executive sponsor is critical. Executive sponsors are a good signal that there is a strategic business objective where AI can be useful, with priority to solve it. Executive sponsors may also provide air cover and force cross team collaboration or resolve data access issues. Keller Williams started with a strategy exercise to connect business challenges to ML models that may help them build advanced capabilities. They started with outcomes they desired both near term and long term. Once they identified the capabilities, they then inventoried the data required, mapping what is owned and their gaps. It’s time for all businesses to build a point of view on how they may apply artificial intelligence across the business. You should get AI ready. Based on how you want to augment processes or create predictions, new data needs emerge. Start with the outcome in mind and connect to the information you need to own to better understand the phenomenon so well, you can predict it.
It is also important to consider your processes for data acquisition and intelligence projects. Many business have a legacy system where a ticket must be generated for any new data projects. This waterfall approach needs to evolve into an agile process. There is a new agile for AI where IT operators who capture data, business intelligence/data science leaders who analyze data, software engineers who build solutions, and DevOps all work on projects collaboratively to build data products.
Plant a tree today.
A chinese proverb said the best day to plant a tree was 20 years ago. The second best day is today. If you don’t know how to derive value from data, or maybe AI is viewed as sci-fi in the business, plant your tree today. Data exhaust is real and so is the future value. Start collecting today and planning for how to use it. The definition of intelligence is taking on new meaning and the future of business will value those who collect the most data, use the most advanced AI algorithms, and act the fastest.
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