• Having a clear enterprise strategy is one of the most important first steps in managing all data and getting value from it.
  • Cloud services allow enterprises to deliver new capabilities with current skills, automatic data encryption and security, data access across systems and locations, and real-time analytics.

Worldwide, organizations are looking for ways to derive as much value from data as they can to serve their customers better, enhance their products, increase efficiency, expand their business and gain a competitive advantage. Data is the starting point for getting insights that can change a business.

While the understanding is there about data playing a significant role, many organizations are mistaken to believe that data alone can lead them to the path of success. In reality, data is just one of the components that can provide insights that can change a business.

So, how can businesses use data to offer insights that lead to generating value? What do the leaders in this field do differently than other businesses? Read on to learn more about how businesses can derive value from data.

Transforming Data into Unbeatable Business Value

As companies progress along their digital transformation paths, it’s becoming clear that data is not the sole competitive differentiator. What matters is what companies can do with data, whether that’s making more money, coming up with new ideas, or giving customers a better experience.

Most firms are receiving and using increasing volumes of internal and external data. Still, a new Harvard Business Review Analytic Services assessment revealed that some are doing so more effectively than others. Leaders use data to stay connected and up-to-date, but such organizations are a few today.

Many big corporations have harnessed data to register success and derive business value, but leading firms are taking a step further to develop a C-level post to supervise data (a chief data or digital officer) concerned with risk mitigation and producing as much value from data as possible. These firms rearrange their technology, process, and people around data, and their activities yield outcomes.

Indeed, digitally transforming organizations can learn from what some leaders do differently. Curious to know what it is?

Well, they are more likely to value and allow the connection of data points across assets and services, real-time data access and analytics, and AI-enabled automation of data-driven insight. They are adapting to multi-cloud techniques in greater numbers. They even periodically measure and generate reports on how data and analytics investments impacted their business value. Many executives claim an increase in revenue and profitability, employee and customer happiness, and new product and service introduction.

A Strategy for Success

For a long time, organizations have been looking forward to IT systems to derive value from data. What’s concerning is that the enterprise data environment has become even more complex today. With volumes of data being produced and much of it being hosted in cloud environments, there’s a need to analyze a considerable amount of data types from internal and external sources. Topping it all, the velocity at which the data is being generated has also spiked.

Without a strategic plan to organize that data and apply it to internal and third-party business challenges, deriving value from it is a laborious and sometimes frustrating task. Manual data manipulation leads to low data usage in most firms, and data scientists spend much time wrangling data.

Having a clear strategy around data management and governance, as well as a roadmap to what an organization aims to achieve with data-driven insight, is essential. Data wranglers play an essential role, but they must focus on a specific task or goal.

This entails the development of data teams that function under a well-defined strategy and data governance structure to bring it all together. Successful organizations that aim to derive value from data create cross-functional teams consisting of data wranglers, enterprise architects, data scientists, AI experts, and data-savvy businesspeople who can identify the problems that data might solve.

Building An Effective Data Strategy

An effective data strategy calls for four crucial components. And when all four components are constructed and aligned appropriately, they offer accurate, reliable data that helps achieve business objectives and offers real value.

Business strategy: Formulate questions that you want the data to address, and then prospect on the KPIs and metrics that will help uncover the answer. You may enquire about the type of content that can help sales, or keep track of the products in the transportation chain, etc. When you are through with defining the questions that need answering, you will chart a path for your data collection.

Roles and governance: It must be ensured that you and your team know about the different responsibilities when it comes to data. Get a thorough understanding of who is responsible for which data, the questions that can be asked, and the types of reports that are available.

Data management: For the success of any business, the data needs to be secure, clean and accurate. Whether internal or third-party data, it must be protected from security breaches, must comply with all regulatory compliances, and be cleansed to avoid out-of-date or incorrect information.

Technology: Be very selective when choosing the tech you want to collect and store data. It must be ensured that it suits your needs without going overboard. You can find the right tech solution by understanding the types of outcomes you want.

The Status of Advanced-Data Competencies

Advanced data and analytics can perform three crucial things — connect data points across various assets, devices, and services; provide real-time access and data analysis; and automate data-driven insights. But how do organizations bring together data not just from their heterogeneous mix of systems but from external sources as well? Real-time analytics is faced with its own set of challenges.

The ability to deploy analysis in a timely manner depends much more on automation – the third capability cited by leaders as crucial for success. Using machine learning or other intelligent automation capabilities can help improve operations. Likewise, greater automation is faced with challenges that are yet to be overcome and calls for advanced machine learning skills and good data.

Acknowledging the need for advanced capabilities to source data-driven information, organizational leaders are willing to overcome the problems connected with these sophisticated analytics skills and adopt them at far higher levels than their peers.

Tools for Getting Value Out of Data

Certain core positions or competencies have emerged as crucial to extract data’s total economic value. Pertinent among them are cloud services that allow enterprises to deliver new capabilities with current skills, automatic data encryption and security, data access across systems and locations, and real-time analytics.

When you look at both limited and broad adoption, the numbers go up, but some organizations are still ahead of the rest. Most often, people talk about being able to access data across all of their IT systems, encrypt data and automate security, do analytics in real-time, and manage cloud services without having to learn a whole new set of skills. Only a handful of firms say they have these skills enterprise-wide.

To conclude

To derive business value from data, firms must integrate data across assets and services, implement real-time data analytics and AI-enabled automation, engage in multi-cloud, and analyze the business effect of their data investments.

All firms must prioritize data-to-value to remain competitive in the years ahead, creating a strategy and platform to turn data into exponential value and teaching workers to use it.