Let the Cloud Data Wars Begin
This post was originally published on the InsideSales.com Blog and was written by myself and CEO Dave Elkington.
We are in the midst of a new global war. This is not World War 3 with weapons of mass destruction. We are not talking about the zombie apocalypse. However, the players are more powerful than governments and the stakes are higher than sovereignty. This a war with clear sides, leaders, battles, bullies, real money, and power at risk. This war is a global war over data—the Cloud Data Wars.
The Reign of Data-centric Business Models
As the Computing Cloud has become organized and ubiquitous, data has been aggregated to deliver collective, global intelligence to feed products and services that improve our lives. These new products and services generate big, big money. Data is now bought, sold, and traded as currency outpacing gold, oil, and even crypto-currencies. The players in the emerging cloud data war are the largest companies on the planet, and in many cases, they have already secretly invested billions of dollars in a race to own the most and the most important data. Much of this is hidden from the public’s view, but we have all benefitted from one of its outgrowths – Artificial Intelligence (AI).
AI has become the buzzword du jour and is being used in boardrooms, investor meetings, and in most product and sales pitches as a way to claim innovation. The easiest way to understand the relationship between the cloud, data, and AI is to consider consumer Internet applications.
Beginning with the advent of Web 2.0, most modern B2C applications are architected from the ground up with data-centric business models. Amazon knows that “people who bought X also bought Y.” Netflix knows what “people like you” enjoy watching. And, Waze knows what drivers 20 minutes ahead of you are encountering in real time. The way these benefits are delivered is simple: Web 2.0 applications collect data from every user, aggregate it, analyze it, and then contextualize it for the benefit of each individual user. Your personalized Netflix recommendation doesn’t come solely from your own viewing history—it comes from the viewing history of “people like you.” Your Waze navigation recommendations don’t come from previous trips you personally took—they come from other drivers who are on the road right now.
If AI is the engine, data is the fuel. In 1998, Google declared its mission to “organize the world’s information and make it universally accessible and useful.” Organizing all the information from the world’s libraries and archives seemed daunting in 1998—but we are so far beyond that now. Almost 90% of the world’s total documented information (data) was created in the last two years, and the pace of data creation is only accelerating with IoT.
From Hardware to Data: The Evolution of Tech Wars
In the 1980s, technology wars focused on chip speed and hardware acceleration. Players like Sun, HP, Intel and their competitors were largely concentrated in and around San Jose, California. They scrambled to raise a few million dollars to fund production runs for their latest inventions. This was the Hardware Era.
In the 1990s, workflows were encoded into software, and investment sizes grew to the tens of millions as players like Microsoft, Apple, and Intuit standardized and scaled major categories of productivity. This was the Software Era.
By the 2000s, the Internet had arrived, and major players parallelized hardware in the cloud and transferred software to this more efficient, centralized architecture. Players like Google and Amazon and Microsoft battled over who would own the Cloud infrastructure, and other players like Salesforce and the whole of the consumer Internet took advantage of the new infrastructure to build new cloud-based business models. This was the Cloud Era.
Today, we live in the Intelligence Era, and billion bets are placed on data. Which data is most important? Who will generate it? Who will collect it? Who will aggregate it, analyze it, and own it? In the Intelligence Era, data is the prize. Whoever controls data, controls intelligence. And whoever controls intelligence, controls commerce. The data economy is global, and control of the right data eventually means control of global sectors of the economy.
Let the Cloud Data Wars Begin
This is not a new phenomenon. The battle over data began over 15 years ago as Cloud applications began collecting and aggregating data through web technology platforms. However, the data gold rush has intensified at an increasing rate, culminating at the 2018 Salesforce.com Dreamforce event. No wonder the world’s largest corporations have entered the fray:
- 2016 – January: IBM buys the Weather Channel for $2 Billion to provide IBM Watson access to the global weather data asset.
- 2016 – June: Microsoft beats Salesforce in a bidding war and pays $26.2 Billion for LinkedIn and its data on over 400 million professionals across the globe.
- 2016 – October: Salesforce (among others) rumored to be bidding for Twitter to get access to global trend data (failed attempt).
- 2017 – March: Salesforce partners with IBM Watson, gaining access to global Weather Channel data among other things.
- 2018 – March: Saleforce pays $6.5 Billion to acquire Mulesoft—a data integrator.
- 2018 – September: Microsoft, Adobe and SAP announce the Open Data Initiative (ODI).
- 2018 – September: Salesforce counters the ODI announcement with its own announcement of “Customer 360”
What does all of this mean?
- It’s all about the data, stupid.
So far, B2B Artificial Intelligence has been the bubble that wasn’t. Sure, it’s fun to think about the B2B equivalents of AI-guided commerce or AI-guided navigation, but AI cannot operate without impressive amounts of data. In the B2B world, a critical mass of data is hard to come by. Since no single company has enough data to fuel AI, the launches of Einstein and Watson in B2B have been more like thuds. One can have all the AI algorithms in the world, and even a platform to run them on. But without critical mass of data those investments will go underutilized.
- Cross-company data is a requirement.
If no single company has enough data, what about “all the companies”? Yes, that would do it. The B2C world solved this by building apps that track all consumer activities and then use that collective data to help each individual make better decisions like what to buy, what to watch, how to get from point A to point B, etc. For B2B intelligence to take hold, we need cross-company data to be collected, normalized, and categorized for analysis. This allows each company to make decisions based on the superset of possibilities, not merely their own history with their own customers (the consumer alternative of which would be if you were the only driver in the world who had installed Waze).
- Follow the money.
If one were to organize the world’s B2B data to optimize business outcomes, where would one start? With cost-takeout initiatives? No. The most lucrative optimizations are ones that drive revenue. This means focusing on Sales and Marketing, which is exactly what Microsoft, SAP, and Adobe have done with their Open Data Initiative.
With points 1-3 above in mind, let’s focus on collective sales and marketing data—the holy grail of the Cloud Data Wars.
One Big Step for Tech, One Small Step Toward AI Sales
Kudos to Microsoft, SAP, Adobe and Salesforce. All four have recognized that customers are frustrated by status quo. And all four have taken a first step toward gathering more data to feed AI systems in an attempt to inform better B2B sales and marketing and better customer experiences. While it is a small step toward optimizing B2B sales, market leaders have declared data to be the prize, and they are moving heaven and earth to organize the world’s B2B sales and marketing data.
AI Value of Data = Breadth X Depth X Quality
The true value of AI is directly proportional to the breadth, depth and quality of the data that feeds it. In today’s information-rich environment, B2B buyers are largely self-educated before engaging a salesperson, and thus buyers hold all the cards. Data about buyers, what they are researching, how they buy and how they engage can give sellers an advantage when vying for limited attention and limited budget dollars. In this case, more and better data about more buyers is the key.
Silos --> Singular Visibility --> Collective Intelligence
There are three basic levels of data intelligence—siloed, singular and collective. Most companies are stuck with Siloed Data, struggling to analyze data stranded in diverse CRMs throughout their organization. Without systems to integrate these silos of data, companies do not have a 360° view of customers and prospects and cannot effectively understand their customers. Integrating data across silos appears to be the purpose for Salesforce’s acquisition of MuleSoft.
Microsoft’s ODI announcement and the Salesforce Customer 360 vision are both trying to deliver Singular Visibility, or insights mined from integrated data throughout an organization—a marked improvement over the siloed data status quo.
The true promise of AI can only be unlocked by tapping into Collective Intelligence—using advanced analytics to uncover predictive and prescriptive insights from the collective actions of millions of buyers throughout the world. Amazon is the best B2C example of this model, using AI-driven Collective Intelligence to disrupt online commerce and become one of the first trillion-dollar businesses.
Unlocking the Promise of AI to Fuel B2B Sales
Unlocking Amazon-like AI recommendations for B2B sales starts with data from a collective universe of buyers. Applying AI’s advanced analytics to global, cross-company, multi-CRM behavioral and experiential data helps the best performing companies develop customer and prospect understanding that has never before been possible.
InsideSales.com has understood the symbiotic relationship between AI and Collective Data since day one of our company, and we have crowdsourced the world’s richest set of 120+ billion behavioral data available to power AI sales.
As we watch the Cloud Data Wars unfold, we are more certain than ever we are in the best position to deliver on the promise of AI with our Collective Intelligence Data.
In the End, the Buyer Wins
Of one thing we are certain: the data arms race will transform buying experiences for the better. More informed buyers and sellers will be happier and more productive with fewer blind spots, less friction and fewer frustrations—three realities that plague sales today. And, as Martha says, “that’s a good thing.”
To learn more about how AI and Data, feel free to download the Frost and Sullivan Report entitled, "How Artificial Intelligence is Disrupting Sales"
All-in on PLG and AI-assisted GTM.
6 年Part 2 of this series now posted here. David Elkington
Chief Awareness Officer at eCoinCore
6 年Absolutely! The "Big Data" buzzword was over hyped and hasn't been lived up to. Aggregators, IT teams, and analyst's manage this world today and many times the information desired simply takes too long to find or the request is not clearly defined. I believe as data becomes easier to directly and personally access for sales and marketing teams, more refined insights will be gained. Semantic data is already making this possible today.
President and Board Member at Techcyte
6 年I work with machine learning for medical microscopy data so it's a little different than sales data, but this article is pretty spot on.? One thing I'd like to add is that those that adopt early will be much better off.? Consider the following hypothetical: Suppose there is a grocery store that has been keeping track of what shelf they store their peanut butter on for the last 10 years for all of its 100 stores.? The store manager then goes to an AI (machine learning) company to request analytics to predict the best place to put the peanut butter to maximize sales.? There is lots of "big data" and so he assumes good predictions should be easy.? Right?? No.? The first thing the AI company will request is all of the peanut butter location data WITH SALES DATA for the same period.? Woops!? Somehow that was never stored anywhere.?? While this story is fictitious, the moral is real: Not only do you need data, you need the right data.? We've been taught to store data for a "big data" play, but in practice if the stored data was never used it is probably mired with incorrect or missing data.? Storing bad data for the past 10 years isn't really any better than storing bad data for 1 year.? A little bit of bad or lot's of bad, is all just bad--you can't buy quality with only quantity. If I were in charge of predicting peanut butter sales, I would actually not use any of the existing data and would use just the next month's location and sales data for all 100 stores.? The data would be clean, complete, and correct and building a model would be both cheap and accurate.? The alternative is you have to gather different data to infer peanut butter sales through some other means like store sales, market trends, stock price, or some other "clever" heuristic.? These alternatives at best would be more complicated and costly, less accurate, and more brittle.?? Does that mean storing the original data was a huge waste of time and money?? I don't think so, it created the corporate?knowledge and infrastructure that allowed the collection of the improved one month data to be easy and cheap.? We are in a fortunate time where the arguments to store big data have largely already been won; it is now time to start cashing in.? That means plugging in your data into analytics systems?sooner rather than later to flush out your deficiencies.? Integrating with other companies and following standards also is a great way to improve data value because it is now usable by more people.?? The more you use the data, the more ways you'll find to improve it.? The more it is improved the more uses it will have.? Each iteration makes your data better and your ROI a little higher.? Once you know your data is being gathered correctly and will be useful, you can put your peanut butter on different shelves to actually learn what variations are useful.? The sooner you use the data, the sooner it will be useful!
Product Marketing Manager | Cybersecurity Enthusiast | Leader | Father
6 年I completely agree with this! The company with the right data and the right capability wins. Aggregating B2B data is the obvious next step for pushing the limits of what can be done since many companies have already mastered collecting and utilizing consumer data. In addition, there are many concerns that are arising with privacy and collecting consumer data that is non-existent with a B2B data collection play.
Strategic Advisor, Investor and Enterprise Sales Professional
6 年Exciting times for sure! ?Our Collective Intelligence sets us apart from the others players and our customer's success speak volumes.