There are many ingredients for a successful digital transformation, including buy-in from the enterprise leadership and clear goals and objectives, but perhaps the most important element is data.
Big data is the lifeblood of digital transformation. Without it, not only will your digital transformation lack power, it simply won’t get off the ground to begin with. It’s impossible to understand what is going on in your organization without unified, accessible data, which gives you the insight into your company’s strengths and weaknesses, which activities need to be connected, where you can add value, what opportunities and risks are on the horizon, and what your customers expect from your organization.
Here are some of the reasons why data is vital for digital transformation.
Create Unified Insights
Digital transformation through data means bringing together all your fragmented data points from every source, both online and offline. Today’s customer journeys are highly diffused, skipping from touchpoint to touchpoint and from channel to channel. They are non-linear, so they are hard to predict, and omnichannel, meaning that without a unified data collection strategy you won’t be able to track all the factors that influence a purchase decision.
Advanced analytics, even those that do not involve artificial intelligence (AI) or machine learning (ML), can produce predictions about customer needs and market fluctuations that will improve the customer experience (CX) you offer, but only if they can access all your customer data.
Enterprises need to break down silos between data collections, ideally by moving to the cloud so that every data source can be accessed by every tool. With these insights, you can plan where to expand your business, which points need to be strengthened, and how to enhance your customer offering.
Enable Effective Knowledge Management
Knowledge management is a way of democratizing access to the highest level of expertise within the organization. A domain expert acquires knowledge through years of experience and discovery, but a knowledge management system (KMS) offers a similar level of awareness and understanding to new hires, swiftly-rising managers, and digital intelligence tools.
A KMS relies on data because data and knowledge are not the same thing. If someone reads spreadsheets with the past decade of sales income, they are still unlikely to understand what drives your sales. Knowledge could be thought of as the insights that can be derived from data, the expertise that employees gain from the experiences they undergo together with the information they absorb.
It’s more than a simple collection of datasets; it’s the patterns and relationships between those datasets that form your knowledge base. That’s why an effective knowledge management system is enabled only by unified data collections that reveal cause and effect, producing integrated, multi-faceted insights that guide business decisions.
Create Useful Enterprise Knowledge Graphs
Enterprise knowledge graphs, or EKGs, are often the next step to producing effective AI models that deliver meaningful predictions. AI tools need to be able to access all of your data collections, but that can be difficult if you aren’t sure where they are.
Enterprise knowledge graphs are like maps of all the knowledge that your organization contains, serving to guide AI tools to find the information they need no matter where it’s located. They help to simplify the process of digital transformation by linking all the data in your organization together, without requiring you to move the data. In this way, EKGs become part of your organizational data too.
Lay The Foundation For Automation And Robotics
For many enterprises, automation is the pinnacle of digital transformation. It’s relevant for numerous use cases, from process manufacturing, to configuring security parameters for IT systems, to easing time-consuming tasks like sending marketing emails or unifying employee expense reports.
Successful automation rests on ML models, which in turn require vast amounts of high quality data that train models to know how a process should be completed and what to do when something doesn’t go according to plan. Data trains and tests algorithms to recognize what is correct and respond adequately when processes are not correct, so they need ongoing quality data to keep them on track.
Deliver Personalization At Scale
Today’s customers expect personalized experiences at every turn, whether it’s the B2B tech buyer looking for a solution that addresses their specific pain points, an ecommerce consumer seeking the best sushi mats, or an industrial customer trying to find the right piece of equipment that matches their budget.
But enterprises need to roll out personalized, tailored experiences for every customer, every time, round the clock. Personalization at that kind of scale requires massive amounts of data, so that your AI and ML tools can quickly divine what each customer is looking for and serve up the content or product suggestion they need without a delay.
Data Is Crucial For Every Digital Transformation
Without data, it’s impossible to produce the unified, actionable insights which form the foundation of knowledge within an organization. Knowledge management systems, enterprise knowledge graphs, smart automation, and personalization at scale all require reliable, clean, trustworthy data that they can easily access and adapt into accurate predictions that drive productivity and profitability.
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