Thank you for joining us on this data-driven journey. We understand that embracing a data-driven approach can sometimes be challenging, but the rewards are undoubtedly worth it. Let us address the most common barriers to adopting a data-driven mindset and empower you to take action.

Introduction

A 2022 S&P Global Market Intelligence survey of data and IT professionals found that, while most respondents believe that their organisation’s decisions are driven by data (to some extent), fewer than 1/4 said that all strategic decisions are data-driven.

The reasons are myriad, and they are not new.

Today’s buzzwords for data are machine-learning and artificial intelligence. But ask yourself, if you can’t trust the data you currently have to drive decision-making, can you really trust machines to make decisions using the same data?

Broadly speaking, the barriers to driving decisions through analytics can be split into cultural and technical barriers. Let’s have a look at some of the most common challenges and how they can be overcome.

Cultural Barriers to Data-Driven Decision Making

Foster Cultural Buy-In:

Cultural resistance to change is a natural part of any process, with both executive and management support necessary to become data-driven. It all begins with laying the groundwork by demonstrating the benefits of leveraging data effectively and building confidence and expertise in data management among your team members.

Tactical empathy, the ability to understand and respond to different data cultures that may be driving differing attitudes towards data across the business, is also critical as it allows one to position a capability in a way that is relevant to different stakeholders.

Another key trend is the ongoing shift towards data democratisation and self-service BI. More users are accessing data for analytics and many of these have relatively low levels of technical expertise. It is important that these users are supported, both from a technology perspective and with coaching and training to help them to achieve their goals and build their capacity.

Next step: Provide everyone with the information they need to understand the advantages of embracing a data-driven culture. Start from the basics, providing concrete examples, use cases, and supporting statistics. Be prepared to address questions and concerns that arise.

Choose Relevant Metrics:

Selecting the right metrics is an ongoing learning process, often involving trial and error. Focus on metrics that directly align with your business goals and drive meaningful change. Begin with a select few and assess how they impact behaviour and reactions. If you do not observe noticeable changes, reconsider their relevance to your overall objectives.

This focus will also ensure that you identify key projects and initiatives that move the business in the right direction, and that support the goal of fostering cultural buy-in

Next step: Clearly define your business goals before choosing metrics. Only with well-established goals can you identify the metrics that will enhance performance and contribute to your desired outcomes.

Act on Insights:

When examining data, seek correlations that link to your hypotheses. However, even when correlations exist, evaluate the level of confidence in those relationships. Look for frequent correlations where the benefits of acting on the data outweigh the risks. Move away from biased assumptions and personal preferences, and let the facts guide your decision-making.

Remember, data comes first, judgment follows.

Next step: Familiarize yourself with your data by conducting tests, experimenting, learning, and iterating. Embrace a culture of continuous improvement based on data insights.

Understand the Impact:

Recognize that the impact and return on investment (ROI) of a data-driven approach may not always be immediately apparent or black and white. It takes time to develop a comprehensive understanding of the benefits. Each business will cultivate its unique data culture, with varying levels of ROI. The greatest payoff lies in making confident decisions based on customer data, reducing the influence of personal bias.

Next step:

From a business perspective, evaluate the time, money, and resources invested in strengthening your data-driven approach. Compare these investments with the milestones and targets you have achieved over the same period. From a cultural perspective, assess workflow processes and individual contributions to identify improvements.

Implement Effective Data Processes:

Creating data processes doesn’t have to be a complex endeavor involving intricate flowcharts or rigid formulas. It can be as simple as an email, a meeting, or a brainstorming session. Every company is unique, and it takes time to develop processes that align with your product, company stage, and stakeholders. Remember that formality is not a prerequisite for success.

Once basic data processes have been defined it may be time to automate data governance processes.

Some simple examples of workflow processes that you may want to automate could include:

• Propose a new {asset} – a simple workflow to allow knowledge workers to propose new data assets that would be useful to them. The could range from policies and standards to reports and data sets, and everyting inbetween
• Data quality metrics and issue management. Defining and approving data quality rules and metrics, and managing any exceptions and issues to resolution.
• Maintaining reference data. Managing the process of adding or removing reference data items from lists and keeping these in synch.
• Data access requests. Managing the process of connecting consumers to data without compromising privacy
• Data breach management. A process to identify and communciate the impact of any suspected breach as per PoPIA.

These are a few examples of many potentially complex data processes that may be sucking time and enthusiasm from your expensive staff. Automation saves time, provides an audit trail and ensures consistency, as long as the workflows can be easily adapted to your way of working.

Next step:

Engage in open and honest discussions with your team to determine the most effective approach for your company’s data management. Stay focused on the ultimate goal: making data readily available and easy to use for every employee. Challenge the status quo while respecting the limitations that may exist.

Leverage Existing Skills:

Do not underestimate the skills that may already exist within your organization. It is a common misconception that only individuals with technical backgrounds can understand and manage data. While such backgrounds can be helpful, the key lies in presenting data in a clear and concise manner that allows anyone to ask the right questions. To do so, individuals need to understand what they are looking for.

Next step:

Move away from overwhelming spreadsheets and find tools that facilitate effective communication and understanding of data. Explain the numbers clearly, giving people the opportunity to interpret and react to the data. Consider enrolling in online courses or workshops to enhance your team’s data literacy skills.

Modern, governed data catalogues provide numerous benefits. Given the significant amount of data involved, and the complexity of modern data landscapes, it is essential to dismantle data silos, enable business users to retrieve their data independently, and address common data challenges using the latest modern technologies.

Technical Challenges

Establish the Right Infrastructure:

There are no excuses for not implementing an information model that enables superior access to high-quality data. Developing a data-centric and information-liberating architecture sets the stage for long-term success, paving the way for future business gains.

Next step:

Evaluate your current data architecture, weighing the advantages and disadvantages, gains and losses. If implementing a new model is not currently feasible, explore alternative means of obtaining the data you require. Start where you can, using what you have.

Uncover Hidden Data:

Discover the valuable insights that lie within your own business. Instead of hesitating, take the initiative to inquire about the whereabouts of your data. Whether it pertains to customer demographics, service data, or digital interactions, you have access to a wealth of information. Leverage a wide range of analytics and optimization tools, many of which are freely available, to monetize this data. The information you seek may be buried within Data-as-a-Service (DaaS) platforms like the Precisely Data Experience, your cloud-based CRM or ERP, or your proprietary systems. The time to utilize it is now.

Next step:

Identify the systems and services capturing and storing your data, and evaluate the usability of each. Consider adopting better tools that provide easier access and cleaner, time-saving data. Seize the opportunity.

Address data quality and trust issues

Data quality remains a major concern for many businesses. However, the nature of these concerns is changing. With the increasing use of data across various users and scenarios, the visibility of data quality issues has never been more evident. While legacy data quality problems persist in many enterprises, new challenges are also emerging.

The growing volume, speed, and diversity of data have made it increasingly difficult to address the issue of poor data quality. The traditional approach of sporadically cleaning up data has proven inadequate in practice. To ensure and sustain high levels of data quality at a larger scale, a proactive and systematic approach is essential. This entails utilizing the right technology to organize data assets, establish common terminology, define clear business rules and data owners, and promptly notify the appropriate individuals when potential issues arise.

Next step: Begin by identifying to or three key business objectives and the critical data elements necessary to support these. What are the minimum data standards necessary to meet the goals?Do you critical elements meet these standards?

For example, let’s consider a retail company aiming to improve its customer satisfaction ratings by implementing a personalized marketing campaign. To achieve this, they plan to analyze customer data such as purchase history, preferences, and demographics. However, due to poor data quality in critical data elements, their efforts are hindered.

One of the critical data elements is the customer’s email address. Unfortunately, the company’s data records contain numerous errors, outdated email addresses, and missing information. As a result, when they launch their personalized marketing campaign, a significant portion of the emails bounce back or fail to reach the intended recipients.

The poor data quality in email addresses not only results in a lower delivery rate for their marketing materials but also leads to missed opportunities for engaging with customers. The company’s goal of enhancing customer satisfaction through personalized marketing falls short because they cannot effectively reach their target audience.

Furthermore, inaccurate or incomplete data in other critical elements, such as purchase history or preferences, can lead to misguided marketing efforts. For example, if the company relies on outdated purchase history data, they might recommend products to customers that they have already purchased or are no longer interested in, leading to frustration and a negative customer experience.

Consider a formal data quality audit on commonly used data sets, to identify and quantify both known and unknown issues. At this level we can look broadly at key data sets used for decision making to provide an unbiased assessment of their usefulness and trustworthiness.

Conclusion

By addressing cultural and technical barriers, you can break down the obstacles that hinder your path to becoming data-driven. Cultivate a data-driven mindset within your organization, leveraging the skills and insights of your team members. Establish the right infrastructure and embrace modern technologies to unlock the true potential of your data. Remember, this journey is not without its challenges, but the rewards that await you are immeasurable. Start taking action today and embark on the path to data-driven success.