Strategies To Drive Digital Transformation From Data

Today, the entire industry is under siege from the competition. This has been the catalyst for companies to adopt digital transformation, to cause a disruptive effect on existing business models, and remove intermediaries in customer relationships from their competitors. But what do we mean by "digital transformation"?

We can briefly define it as coupling real-time data (e.g., smartphones, connected devices, smart devices, handheld devices, mobile commerce, video surveillance) with modern technologies (e.g., cloud-native applications, Big Data architectures, hyper-converged technologies, artificial intelligence, blockchain) to improve products, processes and business decision-making with information from customers, products and operations.

How can you know what you have to transform and when you should do it?

The answer is in the data and an analytical strategy that empowers your collaborators to make discoveries that drive change. All of them must be able to use the data to achieve results such as reinventing business processes, better understanding customers, discovering new sources of income, and better balancing of risk and benefits.

In this note, we introduce you to four ways to use modern analytics that can help you unlock the value of your data, to drive faster and more effective digital transformation.

1. Take advantage of AI and machine learning

Machine learning, a method implicit in most AI, automates analytical modeling and learns from data, allowing you to identify patterns and trends. In other words, machine learning analyzes your data and provides you with insights you might have overlooked.

When machine learning is combined with intuition and human knowledge, you get augmented intelligence, a collaborative method that multiplies intellectual power and improves data literacy for all users.

It is already used in retail, increasing website sales by recommending items based on past customer purchases. Also, in the productive sector: computers analyze data from equipment sensors to identify efficiencies and detect anomalies.

For business users, machine learning offers insight into business apps for sales, marketing, human relation., finance, and other areas.

2. Integrate peripheral analytics

There is no point in limiting analytics to a selected group of people. Rather, it is desirable to extend the power of discovery to as many users as possible and integrate it into all settings, including mobile devices and remote locations. This is peripheral analytics. It allows users to analyze data as it arrives in the same place where it is obtained. That saves time, reduces bandwidth costs, and guarantees data validity.

As an example, retailers analyze point-of-sale data as it is captured to drive cross or additional sales on the go. Manufacturers use peripheral analytics to monitor equipment to detect early signs of deterioration in performance or risk of failure. Security companies use it in video cameras, which analyze images as they are received and send alarms when changes are detected.

3. Allow everyone to explore without limits

All your employees should be able to freely explore the data, allowing themselves to be guided by their curiosity: this opens up the possibility of making unexpected discoveries.

On a modern analytical platform, users can interact in any location, be it visualizations, tables, charts, or filter panels, and even a user interface with global selections, moving in any direction through all the data, discovering new connections and insights surprising.

4. Train your employees through data literacy

Data literacy is the ability to read, analyze, debate, and work with data. When the goal is digital transformation, data literacy must be as wide-reaching as possible and reach all roles and all employees. In this way, a business exponentially increases its potential.
You can also contact a data science company or search for analytical solutions that organically increase data literacy with the three preceding tools: AI and machine learning, analytics at the periphery, and free data exploration.

Conclusion

When all the employees in your company have the ability to make discoveries on a daily basis, it is time to start the digital transformation on a large scale. The results can be classified into four categories: reinvented business processes, improved analysis of consumer preferences, new income opportunities, and more balanced risks/benefits. Are you ready to start the transformation? A business intelligence company can help you acquire the ability to reinvent business processes, better understand customers, discover new sources of income, better balance risk, and optimize benefits.

Harnil Oza

Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.

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