How To Turn Your Organization Into Making Data-Driven Decisions

How To Turn Your Organization Into Making Data-Driven Decisions

Most organizations are now realizing the power of data and analytics. In the past decade, a few pioneering organizations have demonstrated how making strategic decisions based on data-derived facts can push businesses and innovation forward. It’s not only anecdotal; evidence that making data based decisions makes business-sense is piling up. The McKinsey Global Institute has indicated that data-driven organizations are 23 times more likely to outcompete non-data-intensive organizations in terms of new customer acquisition, nine times as likely to surpass competitors in terms of customer loyalty, and more than twice as likely to be more profitable than competitors.

It’s tough enough for many organizations to keep track of their data; it’s much tougher to translate the data into valuable business insights. Before an organization can use data as a core part of their decision-making process, it needs to build a foundation of well-governed and valuable data, commitment from company personnel to integrate data analytics into their business process, and common language surrounding data to ensure sound communication between departments and across the organizational hierarchy. Luckily, many organizations have already taken the first steps and through their experiences, you can learn how to turn your organization into a data analytics powerhouse.

Create a Value-Based Data Ecosystem

Before organizations can start using data, they first need to identify which data is relevant to their business needs and what impact it may have down the road. After relevant data have been carefully selected, they need to become available to stakeholders as quickly as possible. Traditionally, employees have been used to manually marshal data from various sources into reports for stakeholders. This can be very time-consuming and error prone. Data enabled organizations need to streamline and automate their data acquisition.

The Salvation Army is a charitable organization that focuses on providing resources for the poor, disaster relief, affordable clothing stores, family tracing services, health services, and youth groups. The Salvation Army depends on the donations of individuals and organizations to support its operations. In the past, donors trusted that the organization would put their donated money to effective use, but today, donors want to directly see the impact that their donations are having.

The Salvation Army’s management identified a business-need for providing transparency to their donors. After some time struggling to manually compile fundraising and operational data into useful reports, management investigated ways to automate this process. Following an initial trial where reports were made available to a few donors, the technology was quickly adopted throughout the organization and made widely available to donors.

Facing similar needs but in a completely different business context, AllOver Media, an advertising firm and out-of-home media leader, was struggling to organize meaningful data from disparate data sources into useful reports. The organization did not have an IT department or a data warehouse. The core team of employees was left having to gather data from disparate third-party systems like salesforce to generate reports. After investing in a better BI solution, the company benefited from 80% time savings in BI support, consistent report accuracy, and real-time data streaming results.


Find a Common Language

When sourcing data from or talking about data with various departments, leaders need to ensure a common vocabulary around data. An organization’s sales department may be speaking in terms of a fiscal year, while the IT department may refer to a calendar year. Miscommunication leads to bad decision-making. For this reason, it is very important to find a common language.

The University of Notre Dame appreciates the importance of sharing a common language across departments. When setting out to leverage analytics to improve student outcomes, retention and graduation rates the school was aware that university departments often operate as siloed entities that rarely interact with each other except at higher administrative levels. This means that terminology surrounding data may be inconsistent and therefore cannot be aggregated for strategic-level decisions. Notre Dame had representatives from various departments meet and painstakingly develop a shared data dictionary for common data-related terms and metrics.

All organizations will likely face similar challenges in choosing the right language, data, and metrics to support decision-making. It is the responsibility of the leadership to develop and disseminate a useful data-lexicon throughout the organization.

Foster a Data Culture

McKinsey Group reports that the main concern that senior executives express is that their managers and employees don’t understand or trust data-based models of operation and, as a result, fail to use them. Often, the problem arises because data-based approaches do not integrate well with existing business processes and require a substantial amount of change. Analytics tools and the methods involved tend to be designed for experts, and traditional managers don’t find them immediately useful. If leaders want to exploit the most valuable and timely insights from their data, data literacy needs to permeate across the organization.

Leanne Bateman, academic program chair for the Analytics 360 Symposium at Brandeis University, spoke about why employees in academia are not adopting analytics to improve operations. She described academic departments as data siloes that failed to share data and insight throughout the organization because of their highly specialized and thus non-compatible ERP and CRM systems. Workers rely heavily on Excel spreadsheets, and rarely if ever share their results with anyone else. Her solution is to provide employees with tools that are usable and capable of easily sharing analytic results throughout the organization. Enabling employees to interact and share insights will encourage education and communication surrounding data.

Make Data Central to Strategic Decision-Making

Finally, once an organization has laid a solid foundation of accurate, timely, and rich data sources, created a data language unique and specific to its business needs, and fostered a culture around data literacy, data can become a centerpiece of strategic decision-making processes. Data can be used to improve almost any part of an organization. Unfortunately, only a few are fully capable of consistently and intentionally exploiting their data.

An organization that has made bold moves in integrating data into its central decision-making process is Intermountain Healthcare Inc. (IMH). IMH is a healthcare system of 22 hospitals and 185 clinics based in Utah that is aggressively bringing data into its central operations and decision-making processes. Showcasing its dedication to data-based decision-making, IMH is developing a new healthcare system that provides primary care doctors with internal data about their clinical patient outcomes, their patient’s satisfaction, and the rankings of external specialists for referrals. This system is aimed at giving doctors useful feedback about their services and increasing transparency within the medical community. IMH can integrate this system because they have taken the necessary steps to ensure analytics maturity throughout their organization.

The road to data and analytics maturity is not easy, but if an organization takes the time to lay the proper foundation, it can exploit data to revolutionize nearly any part of its business.

Also Read: A Big Market For Big Data

Leave Comment