IDC predicts the datasphere will grow to a trillion gigabytes by 2025. That’s 10 times the data generated by 2016.
In the world of customer database software, that translates to a lot of customer data.
All this data makes parsing signals from the noise an untenable challenge.
While scarcity drives the physical marketplace, the digital world is characterized by overabundance. With less than ten percent of data effectively used, growing numbers of businesses are realizing that Big Data must translate to contextualized insights and predictive analytics to be effective. Advancements in Artificial Intelligence (AI) are making this achievable.
AI and machine learning are emerging as the integral pieces in collecting, refining, and presenting the insights that will lead the next digital transformation. This is echoed in Salesforce's own pattern of acquisition and their messaging around Einstein:
“A new day is dawning for the customer experience, driven by the application of artificial intelligence, machine learning, and automated technologies to CRM data.”
Advances in AI are already clearing a path to make it possible to effectively grapple with rapidly shifting and ever-increasing customer data. The expected economic impact of AI on CRM software will be massive.
In a report commissioned by Salesforce, A Trillion Dollar Boost: The Economic Impact of AI on Customer Relationship Management, the IDC surveyed 1,028 companies to forecast how AI will affect the global economy with regard to CRM.
According the report, the worldwide financial improvement from AI in CRM software is projected to reach $120 billion by 2020 and $33 billion of it from improved productivity alone.
Cognitive systems will elevate data analysis in CRM from retroactive to predictive. Increasingly, we are seeing digital go the way of automation and optimization. Algorithms driven by a new breed of analysts can identify trends and deliver deeper, more relevant insights. This will lead to more significant human activities and improved business decisions.
The next wave of powerful technology will be those SaaS companies that optimize workflows and use AI to automate customer data. This cannot be achieved without tools that give humans the ability to act on insights quickly, across the organization and through applications they are already using.
The need for these advancements is pressing, given the ever-growing quantity of data and increased need for personalization in marketing and sales efforts. Market-driving companies will be those that successfully manage the dizzying quantity of data and cull meaningful insights from massive data sets.
Per the IDC report, perhaps most indicative of what will characterize the tech landscape for the next decade, “it’s clear from IDC’s forecasts -- and the survey for this project -- that adoption [of AI] is about to begin in earnest.”
“Indeed, the business world is now entering a golden age of AI,” Salesforce writes.
Yes, we are fast approaching a new era of the data age, but there is a large elephant in the room that must be dealt with before this “digital transformation” can truly begin in earnest: CRM software still has a data quality problem.
While AI is indeed the new bright shiny object touted to alleviate the pains of grappling with customer data, the inconvenient reality of AI is that it won’t work with dirty data.
In machine “learning,” machines don’t necessarily learn, as much as they train. AI is only as good as the data feeding it, and insights are only valuable if they come from data sources that are validated and optimized. The learning and subsequent intelligence is formed and entirely reliant upon its data foundation.
For adoption of AI to have a meaningful impact on business, the data foundation it is layered on must be solid and clean. “Garbage in, garbage out,” still applies, regardless how much you try to dress it up.
Already, businesses are bogged down by the amount of data in their CRM -- according to an Experian data quality report, an estimated 3 out of 4 businesses believe their customer contact information is incorrect. Imagine the scope of “garbage” when you consider how fast businesses are hiring, firing, investing, merging, and moving. Data decays practically at the same rate it is put into a CRM.
Refining and updating perpetually dynamic data is a fundamental step in building out the strong data foundation required for AI integrations. In SaaS, cloud-based Data-as-a-Service is a requisite for organizations to enter this “the golden age of AI.”
“Under the strong influence of the current AI hype, people try to plug in data that’s dirty & full of gaps, that spans years while changing in format and meaning, that’s not understood yet, that’s structured in ways that don’t make sense, and expect those tools to magically handle it,” writes Monica Rogati in The AI Hierarchy of Needs. “Maybe some day soon that will be the case; I see & applaud efforts in that direction. Until then, it’s worth building a solid foundation for your AI pyramid of needs.”
If “a new day for customer experience,” is dawning -- with adoption of AI at the forefront -- than the businesses positioned to endure the digital transformation will be those with strong data foundations.
With so much data rendered ineffective, and a shifting digital economy, intelligent data is the emergent digital capital.
Perhaps we will soon see cloud-based data provider integrations with CRM take center stage.
Is your CRM AI ready?
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