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The Data Differentiator: How Data Is Driving Digital Transformation

Today’s businesses are undergoing a digital transformation. The Internet of Things (IoT) is making smart homes, smart factories, and smart cities possible. Autonomous vehicles are changing the transportation industry. Artificial intelligence and machine learning are enabling predictive approaches to decision making and driving business insights.

This digital transformation that is sweeping industries by storm would not be possible without data. Data is the enabler of new technologies and solutions. Data is where important and actionable business insights are derived. Yet, most executives and decision makers are concerned about the quality of data on which their solutions and insights depend. Many enterprises and decision makers fail to understand what constitutes quality data and how it can be obtained, generated, collected, and utilized.

In this post, we will explore how data — or more specifically, quality data — is the critical differentiator that is driving digital transformation and what constitutes quality data.

How Data is Driving Digital Transformation

Organizations are embracing digital transformation because of the benefits it offers. For instance, retailers with their own e-commerce websites, or those selling through third-party websites like Amazon, not only open up new sales channels, but also reach new markets and demographics. The Internet of Things is not only making homes smarter, but also making factories and businesses more efficient. The field service industry is equipping their service engineers with mobile phones, vastly improving not only their mobility but also ensuring that they are able to provide the right service with the right tools at the right time with the help of real-time access to information. But, underlying all these benefits of digital transformation is… data.

Data Optimizes Sales Channels

The potential for reaching new customers is a critical factor in the adoption of digitization. But, the digitization of sales channels or digital transformation is simply a response to customers’ ever-changing preferences. For example, the widespread use of smartphones and faster internet speeds changed the way consumers purchase products and avail of services. Enterprises needed to respond by transforming their sales channels and adding e-commerce websites and mobile apps to their traditional channels such as brick and mortar stores.

Data is key to understanding customers and their preferences. Structured data such as those coming from CRM systems help organizations generate insights on their customers based on their past purchases and historical transactions. Organizations can also gather unstructured customer data from social media and listen to what their customers want through their posts, feedbacks, reviews, and online sentiments. This greater understanding enables organizations to optimize their sales channel strategies to fit their customers’ needs and preferences.

Furthermore, customer data helps organizations tailor their sales channels for more personalized services and engagement. For instance, a customer’s purchase history enables an organization to give that customer some personalized recommendations based on his or her past actions, thereby maximizing upsell and cross-sell opportunities.

Data Drives Innovation and Revenues

Another key to the adoption of digitization is product innovation and revenues. Digital transformation enables organizations to create products that customers want rather than creating products and forcing them upon customers.

Data about when, how, where, and why products are used gives product engineers, designers, and manufacturers insights on how to improve and innovate their products. For example, a company used social listening to understand why its sales are dropping. By listening to and analyzing unstructured customer sentiments on Facebook and Twitter, they discovered that a competing product introduced a new functionality which it lacked. When it looked at the structured data in its CRM systems, the same reason as to why customers are dropping the product was revealed. The company then responded by adding the same function to its product and its sales recovered. Kissmetrics also suggests that to create truly innovative products, companies must look at data and find the gaps between what customers want and what they and other companies are already offering.

In addition to innovating with new and existing products, data helps organizations to discover and capture new opportunities. Data enables organizations to predict trends, from consumer spending patterns to macroeconomic trends, enabling organizations to pool their resources and put themselves in the best position to be the first movers in emerging and future markets. For example, Yo! Sushi uses customer data to predict customer habits, which allows it to determine its price and product promotions throughout the entire year, putting itself in a prime position to maximize opportunities and revenues.

Data Improves Efficiency

As the business landscape becomes increasingly competitive, more and more companies can no longer afford inefficiencies that cost them time and money. Driven by data, digital transformation enables organizations, especially those with high-value assets, to improve operational efficiency.

For instance, more aircraft and manufacturing equipment are being equipped with sensors that measure operating performance. A single commercial aircraft can be equipped with sensors that can generate 20 terabytes of data after an hour of flying. This enables airlines to come up with preventive maintenance plans and extend the life of their aircraft. The same is true for manufacturing companies. The data gathered by sensors in factory machines and equipment enables them to determine their own schedule of maintenance and automatically alert the supply chain and service engineers to ensure the right service is performed, and the right personnel and the right parts arrive at the right time.

Data also enables organizations to optimize their asset utilization. Analyzing historical data provides manufacturers with insights as to the equipment’s optimal configuration such as temperature, pressure, electricity, and workload. It also helps manufacturers predict the demand for their products, enabling them to perform critical maintenance procedures during periods of low demand so that outages can be prevented during periods of high demand.

What Quality Data Looks Like

Business intelligence is only as good as the quality of the data used to generate them. It is not just data, but quality data that fuels digital transformation. It is quality data that enables organizations to find new markets, discover opportunities for improvement, and make good business decisions. However, what quality data looks like is not often clear to organizations. This affects the insights they generate and, thus, they fail to maximize the benefits they get from digital transformation:

In a nutshell, quality data has four characteristics:
Correct

  • First and foremost, quality data is accurate. Quality data maintains its integrity regardless of how many times it has been transferred from one database to another. The correctness of data also factors in its timeliness. For example, suppose a customer changed his or her address. Field service engineers and technicians would suffer in case the data was not updated in the company’s database in a timely manner.

Complete

  • Quality data captures all information relevant to decision makers without bias or error. Missing information can lead to the wrong insights, decisions, and actions. Suppose a survey omits negative responses in its results, whether voluntarily or involuntarily. It would give a rosy, but erroneous picture of what is being surveyed.

Comparable

  • Quality data follows a standard or format enabling it to be compared to other data sets or easily be used by programs and applications. This allows data to be understood and shared across different programs, organizations, regions, and even languages.

Credible

  • Finally, quality data comes from authoritative and credible sources. The consistency of a data source’s correctness, completeness, and comparability is factored in when testing for credibility. With credible sources, data can be trusted and used confidently to generate valuable insights.

Embrace Digital Transformation

In today’s hyper-competitive business environment, embracing digital transformation is a requirement rather than a luxury. Digital transformation enables organizations to open up new sales channels, find new markets and opportunities, increase their revenues, and improve their efficiency. But to fully embrace digital transformation, enterprises must start with capturing, integrating, and utilizing quality data.

OpenText’s Enterprise Data Management Platform ensures that quality data is supplied to the enterprise. Using its ALLOY platform, OpenText delivers a comprehensive data management solution that cleanses, harmonizes, enriches, secures, and consolidates data coming from all corners of the enterprise. As an enterprise’s big data repository grows in the ALLOY cloud, so too does its ability to provide consistent, quality data to applications, stakeholders, partners, and analytics. With a steady supply of quality data, the enterprise can fully embrace and reap the benefits of digital transformation. Contact us to learn more about OpenText’s Enterprise Data Management Platform.

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OpenText

OpenText is the leader in Enterprise Information Management (EIM). Our EIM products enable businesses to grow faster, lower operational costs, and reduce information governance and security risks by improving business insight, impact and process speed.

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