for W3c validation
It’s 2017, and everywhere you turn artificial intelligence is being touted in some portentous- or potentially overhyped- way. AI is the future. So is the cloud. And SaaS. Plus DaaS. Throw in machine learning and you’ll change the world.
But what is actually holding these tools together on the back-end? More often than not, it’s data. So, it’s data that we should be thinking about before we even start talking about the “sexy” stuff.
Dynamic data demystified
A dynamic data foundation is the fuel that runs through the engine of today’s most successful companies – and is what powers successful AI and machine learning efforts. A dynamic data foundation is achieved when all of a company’s data is mastered- connected, structured and accurate within a system that links it across an entire enterprise in real-time. Every organization should strive to become data-inspired. Here’s an example of why:
Imagine a software company that targets small business owners across the country, but has multiple product lines in disparate locations. One product line focuses on tax software and is located in the Midwest. Another is focused on payroll management and is based in the Northeast. Presumably, there are a lot of cross-selling opportunities between the two products and offices, both of which have separate sales teams. Here’s the wrench: both offices are working off of outdated sales systems that aren’t linked, and neither system has been cleaned in over 10 years. Implementing a master data strategy might lead this company to the discovery that its 60,000 contacts are actually closer to 33,000 when you remove outdated, inaccurate and duplicate data. After linking contacts between the two systems, another 10,000 cross-sell opportunities could be revealed.
Revealing these business insights and uncovering sales opportunities can only be accomplished through an organized data structure. To make decisions that impact their business, leaders need actionable data with real-time connectivity that is updated seamlessly and efficiently.
It’s true that AI and machine learning efforts can propel your business – but these technologies can only add value when fed with clean, actionable data. Data is the key that unlocks the door to the emerging and disruptive technologies that can change the way businesses operate.
So, how do you ensure that your data is prepared for these advanced technologies? It all comes down to cleaning, structuring and standardizing it.
High-quality fuel for peak performance
The process for bringing your data to a dynamic level starts with structure. A standardized structure needs to exist at four distinct levels across your database: entity, hierarchy, segment, and location/market. You need to connect your own data with a defined structure and then share that across systems. There is no connectivity without coverage. Data must represent an entire business universe: customers, prospects, and partners across geographies, industries, and segments. The final factor is quality. Data must be trusted as being complete, accurate and current.
The ultimate goal of any master data strategy is to increase revenue, improve leads and break down internal silos. According to Dun & Bradstreet’s State of Sales Acceleration report, B2B sales professionals agree: 85 percent of those surveyed stated that having the right data saves time and increases efficiency. Additionally, with 26 percent of marketers currently using AI and another 22 percent planning to adopt it within six months, it is clear that practitioners need access to accurate data to kick-start the implementation of new technologies.
Ensuring your mastered data is accessible in real-time, no matter the region or department will help you finally access the buzziest technologies of our day to break down silos and increase selling opportunities. Some still seem to think AI is a chicken and egg scenario, but we know what must come first: dynamic data.
Original post appeared at Digiday