Category Data Science
trending enterprise data strategies 2023

7 Enterprise Data Strategies for Effective Business Operations in 2023

trending enterprise data strategies 2023


“Data is great, but the strategy is better.” – Steven Sinofsky

Data forms the main crux of any business. It forms the pulse of enterprises. With changing times and evolving businesses, there is ample of data each organization carries along. Undoubtedly, all organizations, be it any size, or any segment need a proper data strategy in place. Managing data in an unstructured manner isn’t going to yield any results. Merely collecting, storing, and availing data may not give any insightful information for effective decision-making.

Understanding the nuances of framing a proper data strategy, finalizing what to include in that, and finally, perceiving the further road map to execute those steps is what can lead to an ideal enterprise data strategy.

It is evident that having a well-thought-of data strategy that defines processes, people, technologies, and the environment needed to effectively manage different data assets is highly recommended. This data strategy must evolve with time and changing trends. These trends are interesting to observe and help big time in ascertaining the best of enterprise data strategies.

Before we have a look through the latest trends in enterprise data strategies, let us understand what it means and their significance.

What is an Enterprise Data Strategy?

An enterprise data strategy is an end-to-end vision for a business to leverage the best of its data capabilities. It includes multiple strategies that deal with data like BI, big data, master data management, etc.

An ideal enterprise data strategy must be appropriate, practical, easy to use, continually changing with time, and integrated with all relevant components. It supports organizational objectives and business strategies to ascertain how the data can generate optimal value. It can be utilized to extract maximum benefit from numerous data sources like social media, sensors, IoT devices, data files, spreadsheets, multimedia files, cloud-based resources, etc.

A clear-cut enterprise data strategy ensures data literacy, data governance and data quality in an organization. It helps businesses in smoothly progressing from the workings to today to that of the future with least hurdles and optimal efficacy.

Why is an Enterprise Data Strategy Crucial for Business?

Here are evident reasons why having a detailed enterprise data strategy is vital for organizations:

  • Creating priorities with current data sources for better decision making
  • Establishing a relation between logical and physical data structures
  • Charting out a roadmap to understand all relevant systems and their modernization
  • Coming up with areas of data integration and data quality for consistency and accuracy
  • Extract, analyse and resolve all risks involved and come up with a risk assessment and mitigation strategy
  • Ensuring correct responsibilities and associated actions with all data components and governance processes
  • Assisting businesses in achieving organizational objectives with modern-day processes
  • Keeping the huge volume of data in control and go in for effective data management

7 Key Trends for Enterprise Data Strategies in 2023

  • Managing Real-time Information to Become an Almost Essential Ingredient

Data is inevitable now and when it comes to the bulk of real-time data, there is no option but to make the most of it. Organizations have understood the significance of analysing such real-time data and the amount of benefit it yields in business operations and an insightful future.

While deciding on enterprise data strategies, business owners must now take into consideration the roadmap to collect, manage, analyse, test all the types of real-life scenarios that come along. Yes, the complexity levels may increase and as the technology spread widens, managing it may also get tougher. But there is no option but to take advantage of latest technologies and extract the in-depth information from the huge data bulks.

  • Data From Varied Sources Is Now Being Treated as a Result Yielding Product

Data is being fetched and analysed from so many different sources. Businesses are now attempting to get the data to a new intelligence level through latest technologies so that it can be treated like a product that offers high-end business prospects. There must be detailed roadmaps and list of KPIs that must be derived that can explain how the data can be treated as a product. This helps organizations in gaining more control and traction over business aspects, with increased profitability and productivity.

  • Multi-Cloud Platforms to Progress Rapidly with a Variety of Data Strategies

The overall demand for multi-cloud platforms has increased multi-fold during these years. And the coming year shall witness an exponential rise in usage of cloud-based services. Organizations today are depending upon multi-cloud environment in which there are many cloud computing systems that are doing activities such as data analytics and storing of data. This offers organizations the robustness and freedom to execute business activities from anywhere, irrespective of location and geography.

  • Unstructured Data to Unlock New Avenues for Business

The trend of collect data in its raw and unstructured format is increasing. Businesses are understanding the importance of such basic data and the importance of extracting important information out of it through cutting-edge data analytics and data engineering.

It does take a longer time to perceive this format of data but if done wisely, it can bring in a great deal of vision and growth to the business. Technologies like AI and ML are helping organizations extract valuable information from unstructured sources like videos, audio files, emails, webpage etc.

  • Access to Data Is Now Demanded by Inhouse Departments Too

Earlier, data was needed and analysed by business owners, stakeholders, top management, data scientists, data analysts etc. No longer! A time has come when internal teams and their members demand access to data and a variety of reports that can give them a transparent visual overview of business operations.

Organizations must take this into consideration and derive secure and effective ways and means to avail access to data to inhouse teams, as well. It is vital for the IT staff to keep an eye on adherence to regulatory norms and compliance standards.

  • Data Fabric Architecture to See a Rising Demand

A data fabric architecture is a pool of services that offer robust competencies across different multi-cloud infrastructure. It has been used by multiple businesses to streamline data management activities across different environments like cloud, on-premises or on devices. It offers a great merger of traditional data practices with modern-day technologies thereby offering high-end efficiency with reduction in costs and human errors.

  • Product Teams Are Being Cross Connected Across Sectors

Now that data is being treated more like a product, the internal teams have started interacting more with each other, exchanging data, views, strategies etc. Data is now an integral part of all teams and hence they are cross functional with each other. All department including IT, HR, finance, projects, marketing etc. act together and come up with a data product that can be created, tested, marketed, and enhanced.

Wrapping It Up

Are you trying to manage hordes of data in your organization?

Do you feel there is much you can avail from your data bulks but are unable to do so?

If so, contact us. We @ Ridgeant can assist you in extracting and implementing the best of enterprise data strategies that are apt for your business.

Our experienced data consultants implement modern-day services like data engineering, data analytics, product engineering, data warehousing, cloud migration, RPA, AI & ML to help you take insightful decisions for your business.

Get in touch with us and we will help you transform your data into insightful information for accelerating business.


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