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Over the past few years, becoming “data-driven” has emerged as a popular objective for organizations across a variety of industries. Simply put, being data-driven involves leveraging existing operational and customer data as well as external data sources for both tactical and strategic decision-making.

Such an approach complemented with a well-defined strategic vision understood by all company constituents can help organizations better determine where to invest in new partnerships, products and employees. Data-driven organizations are able to effectively compare the success of different geographies, and can manage prospect and customer relationships more productively than enterprises lacking a focus on data.

Overcoming a Dependency on Intuition and Siloed Data

Certainly, an element of intuition is necessary when it comes to an organization’s decision-making processes. In fact, incorporating past experiences, personal perspectives and real-world insights is essential to forming any sound decision. However, to reap the benefits of becoming a data-driven enterprise, objectively consulting and understanding data is paramount. Then, and only then, should intuition and personal opinions be leveraged as supporting considerations.

Too often, organizations rely solely on gut feelings for making business decisions. Moving away from legacy processes and changing “but we’ve always done it this way” mindsets can be difficult, and many organizations are overwhelmed by silos of scattered data. By spending the majority of their time and resources on costly data migrations, organizations lose their ability to execute on any data-driven objective or decision. Furthermore, siloed data is a substantial roadblock to making large volumes of data actionable, comparable, reliable and timely.

7 Best Practices for Promoting a Data-Driven Mentality

To realistically become a data-driven enterprise and ensure the benefits of a data-centric approach are realized over the long-term, organizations should adhere to the following six best practices:

1. View data as a shared asset. Rather than allow departmental data silos to persist, organizations need to ensure all stakeholders have a complete view of all company data. In other words, everyone should have a 360-degree view of all customer and operational insights along with the ability to correlate valuable data signals from all business functions, including areas like manufacturing and logistics.
2. Provide user-friendly data interfaces. For both (human) users and systems to benefit from a shared data asset, it’s critical to provide interfaces that make it easy for all users to consume the data. Whether it’s in the form of an OLAP interface for business intelligence, an SQL interface for data analysts, a real-time API for targeting systems or the R language for data scientists to apply machine learning algorithms and artificial intelligence, data interfaces should be targeted toward users and designed to seamlessly help them perform the job in question.
3. Virtualize disparate data to provide a single data service to the business. Often overlooked, a virtualized single data service allows business users to conduct interactive and multidimensional analysis on their company’s data using the BI tools of their choice. Companies that virtualize their data are able to provide a single interface for different departments and business units to get consistent answers to their queries, accelerated query response time, reduced query costs and protection of sensitive data from unauthorized users.
4. Ensure security and access controls. Thanks to the emergence of data platforms like Google BigQuery, Snowflake, Amazon Redshift, and Hadoop, enforcing data policies and access controls directly on raw data (versus in a web of downstream data stores and applications) has become a necessity. Data-driven organizations should implement technologies that allow IT teams to architect for unified data security and deliver broad self-service access, without compromising control.
5. Establish a common vocabulary. Equally important to creating a company-wide, shared data asset is ensuring that users of the data analyze and understand it using a common vocabulary. Otherwise, more time will be spent disputing or reconciling results than driving improved performance. For example, product catalogs, fiscal calendar dimensions, provider hierarchies and KPI definitions should all be common, regardless of how users are consuming or analyzing the data.
6. Curate the data. Without proper data curation (i.e. modeling important relationships, cleansing raw data and curating key dimensions and measures), data users will have a frustrating experience, which can contribute to reduced perceived and realized value of the underlying data. By investing in core functions that perform data curation rather than allowing self-serve data access to raw data stores in clusters, users stand a better chance of realizing and promoting the value of the shared data asset.
7. Eliminate data copies and movement. The reality is, every time data is moved, there’s an impact on cost, accuracy and time. By eliminating the need for additional data movement by implementing multi-structure, multi-workload environments for parallel and scalable processing of massive data sets, organizations can significantly reduce the costs of moving data, increase data “freshness” and optimize overall data agility.

Long-Term Success Requires Cultural Change

In 2015, over 60 percent of the decisions made by enterprises were based on intuition or on the experiences of their executives. With exponentially increasing amounts of data, unprecedented access to technology, and an increasingly competitive market, it’s imperative that organizations evolve past relying on intuition and start making data-driven decisions.

Investing in the right analytics software can help organizations become more data-driven, however inciting cultural change is crucial for success. Bring in leaders who can help bridge the gap between business and IT, encourage interest among employees to be more data-driven by providing them the opportunity to be creative and involved, virtualize your company’s data analytics and Data Science skill set, and always apply any changes gradually yet steadily. In doing so, organizations can transform into data-driven enterprises and establish a solid foundation for building a modern Data Architecture that will scale along with future business growth.