Data in, insights out: why AI needs robust data to be effective
By Leor Distenfeld, Global VP, Outside Insight
Key takeaway: As the race to implement AI tools at an enterprise level reaches new heights, it’s important to note that the data informing those tools is of paramount importance. Relying only on internal information to inform algorithms will produce insights gleaned only from the information you already have. Rather, it’s vital that decision-makers also look to insights from external data for a much more comprehensive and unbiased view of their customers and industry landscape.
In the growing AI market, International Data Corporation predicts global spending is expected to increase 50% per year, to a total of $57.6 billion by 2021. Business leaders are catching on to the importance of implementing an AI strategy globally. However, it’s not enough just to introduce AI-driven tools; you need the right data inputs to find valuable insights.
During the global launch of the Outside Insight book, author and Meltwater CEO Jorn Lyseggen, alongside AI experts, discussed the importance of the data fueling AI, and the need for executives using AI outputs for decision-making to both understand the data informing those outputs and ensure it’s as comprehensive and unbiased as possible.
“Artificial Intelligence is your rocket, but data is the fuel. You can have the best algorithms in the world, an amazing rocket, but you’re only going to get as far as your data gets you. Data is fundamental — data is AI,” said Gerardo Salandra, Chairman of the AI Society of Hong Kong and CEO at Rocketbots, at the Hong Kong launch event.
“My biggest concern with AI,” Jorn said at the launch event in New York City, “is that people believe too much in it. AI is fundamentally biased in how it was created, trained, programmed. I think one of the most important things for AI to be successful is that executives and decision makers have the data science literacy to beat up the model, to challenge the model, to massage the model and to fully understand what the underlying assumptions are to make sure the answer it produces actually matches the terrain that you want to operate in.”
What’s vital to get the best predictive models and forward-looking insights is that the data informing them comes from a variety of external sources.
The danger of internal bias
Internal data is inherently biased. It says more about you and your organization than anything else. As well, it’s historical. Things like last quarter’s sales results or last month’s marketing campaign efforts, while important to understand and learn from, are limited to what’s already happening within your company walls, and they tell only part of the story.
Inputting data from your own sales team, your own marketing efforts and your own existing users to AI will output insights that are based only on what’s happening within your company walls. Rather, gathering customer data, competitive data, and data from conversations taking place online can help you enrich the algorithms, enabling you to discover gaps and opportunities in the market, predict your competitors’ next moves and better understand your customers.
External data insights: luxury fashion
The need for external data to power AI-driven sales tools
A recent post from the Wall Street Journal illustrates the issue with using only internal data to inform many of the AI-powered sales optimization tools on the market.
When it comes to your sales team’s ability to more effectively engage with leads, using the information contained within internal CRMs can be limiting. It tells only a small part of the customer story. In order to really understand a potential client and his or her needs, the team needs a much more in-depth analysis, which can be informed by that customer’s data and behaviors from a 360 degree perspective.
“AI and machine learning requires massive amounts of data to learn,” Stephen Messer, co-founder of business-intelligence platform Collective [i] told WSJ. Relying heavily on internal sales data is a “really weird approach that captures only half of the equation.”
The “challenge, in terms of CRMs, is that you only get out what you put in,” said Eric Danetz, chief revenue officer of global weather forecaster AccuWeather Inc. There’s so much information about a customer hiding within the online breadcrumbs they leave behind — information which might otherwise be missed.
AI in marketing: why analyzing online conversations is crucial
We all know social media is no longer a tool used primarily to communicate among friends. There are insights and rich data sets hiding within conversations that take place online, and they are happening at an incredibly rapid pace. AI can help sift through all of this data to find real insights lying outside of your company’s walls — if you’re looking out for this information.
Your customers, your potential customers, your former customers and your competitors’ customers are freely and openly expressing what they think of your brand, what their needs are in your industry and what challenges they’re facing. This is free market research. If you’re not listening, you’re not benefiting.
David Arnoux, CEO of Growth Tribe, tells us about the shift we’re seeing thanks to the permeation of AI, particularly in the marketing industry. “We’ve replaced Don Drapers with spreadsheets and attribution models,” he says. “There’s no excuse for not measuring ROI with all the data available today. Machine intelligence for marketing is taking over analytics, taking over design and will take over strategy at some point. It will make our lives better because we can spend more time on the creative stuff.”
Outside Insight: looking in alone is no longer an option
Outside Insight is a new software category — one that will become absolutely crucial for every single business moving forward. As the conversation around data analysis continues to take center stage, it will be those leaders who are constantly looking at external data for competitive and customer insights that will find themselves at an information advantage over those who continue to naval-gaze.
But it all has to start at the top.
If your leading executives or managers don’t understand these tools and technologies, there’s a big chance your company will be overtaken by others that are faster, smarter and earlier to adapt.
This story is published in The Startup, Medium’s largest entrepreneurship publication followed by 315,628+ people.
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