How to succeed in your Data-Driven Transformation in 7 steps?

Every day, executives and senior managers face critical decisions that can impact their business positively or negatively. Decision-makers need accurate and timely information to make the best decisions possible. As data allows companies to identify problems and opportunities they may not have otherwise been aware of, they must base these decisions on solid and robust data.

This is where data-driven transformations come into play. Data-driven transformation (DDT) is also known as data-driven decision making (DDDM), data-driven management (DDM), and data-driven operations (DDO).

The field of data-driven transformation has been around for many years, but it has only recently become a focus for executives and senior leaders. The recent growth of automation and artificial intelligence has made it possible to use data in new ways and provide a significant return on investment. 

For instance, data is essential in taking customer relationships to the next level. Leveraging data can help build more personalized and nuanced relationships with clients, leading to increased customer loyalty and satisfaction. In fact, Chandra Mostov from Wunderman Thompson said it best: “In marketing and data, we tend to look at the averages, but actually the interesting point about humans is that we are all different.” (1). Harnessing these differences through data allows brands to have a more intentional and connected experience with their customers.

There are many benefits to implementing a data-driven transformation, including:

• Increased Efficiency 

Data-driven transformations are more efficient because they automate the process of data collection, analysis, and decision-making. Automation and artificial intelligence can help automate routine tasks, freeing up employees' time to focus on higher-value activities and generally a more efficient allocation of resources. 

• Improved decision-making 

Access to timely data-driven insights based on accurate data on time can help leaders make better decisions faster and achieve better results and strategic goals. 

• Enhanced ability to predict future trends 

By analyzing large amounts of data, we can identify patterns and trends that would otherwise go unnoticed. This information can help you make smarter choices about running your business.

• Improved Accuracy 

Automated processes result in more accurate decisions because they remove the human bias from the equation. 

• Increased ROI

Data-driven transformations provide a higher return on investment (ROI) because they improve accuracy and efficiency.

• Increased Agility 

A data-driven transformation can help you become more agile. You can dynamically change your business strategy by quickly gathering and analyzing data. 

• Reduced Costs 

Automation and artificial intelligence can help you streamline processes, reduce waste, and improve accuracy, meaning cost savings for your business.

At the same time, there is a lack of understanding of what data-driven transformation means and how businesses can use it to improve their performance. There is a fear that automation and artificial intelligence will lead to job losses. Furthermore, the return on investment for data-driven transformations is not always clear. 

However, before executives and senior leaders reap these benefits, they must understand how to carry out a data-driven transformation. The biggest problem companies face when implementing a data-driven transformation is figuring out how to get started. There are many ways to leverage a data-driven transformation, from improving business processes to creating new products or services, and it can be challenging to know where to start. 

In short, a data-driven transformation requires data, automation, and artificial intelligence to inform decisions and drive change successfully.

This article will provide an overview of the steps involved in a data-driven transformation and offer advice on completing each step.

You need to define the business objective you want to achieve with your data-driven transformation at a macro level. Then, identify the key processes and data points you need to collect and analyze to achieve that objective. Exploit data to understand how the business is performing and make decisions about improving its performance. Use automation and artificial intelligence to analyze and model the data to identify patterns and trends. Use those findings to decide how to improve or optimize your business processes. Measure the impact of your changes and adjust your approach as needed to continue achieving your objectives.

How to proceed with your data-driven transformation?

There is no one-size-fits-all answer to this question, as the steps for a data-driven transformation will vary depending on the specific business and its goals. However, in general, there are seven essential steps that companies should take to achieve a successful data-driven transformation:

1. Establish a clear goal and roadmap for the transformation
2. Develop a data strategy
3. Collect, organize, and analyze data into a centralized repository
4. Evaluate your data skills and infrastructure
5. Create models
6. Put data into action 
7. Monitor and evaluate results

Let's take a closer look at each of these steps.

1-Establish a clear goal and roadmap for the transformation

Identify which areas of your business would benefit from a data-driven transformation and begin to plan out how to implement one. You need to define the business problem clearly first and then understand how a data-driven transformation can help solve it.

Your data will never tell you what the business problem you need to solve is. 

Define the business challenge or opportunity; this could be anything from improving customer service to reducing costs. 

Leadership commitment and sponsorship are essential for any successful transformation initiative, including a data-driven transformation. The executive team must be fully behind the effort and provide the necessary resources to make it happen. Sponsorship from senior management is key to getting the buy-in and support of other parts of the organization and ensuring that the transformation initiative remains a top priority.

2-Develop a Data Strategy

This strategy should identify the business objectives to achieve using data and define the necessary data resources and processes.

It is important to note that you should not develop the data strategy in isolation from other business plans and strategies.

Instead, it should integrate all aspects of the business.

You also need to develop a data governance framework to ensure that data is quality controlled and access appropriately managed.

Data governance is the process of ensuring that data is collected, processed, and used in a consistent and controlled manner. 

Data governance is also essential for a successful data-driven transformation. Implement a data governance strategy to ensure that everyone in your organization can use the data effectively. Data governance establishes policies and procedures for managing and using data. It ensures that the right people have access to the right data, that the data is reliable and accurate, and is used effectively to achieve business objectives. 

Effective data governance requires establishing clear policies and procedures for managing data. The organization's management team must enforce these policies and procedures. The management team must also ensure that employees understand the importance of data governance and comply with the organization's policies and procedures.

Usually, a few tools help organizations govern their data:

• Data quality assessment tools help organizations identify and correct errors in their data.
• Data cleansing tool: these tools help organizations clean up their data by identifying and removing duplicate records, correcting inaccuracies, and standardizing values.
• Master data management (MDM) solutions help organizations create a single view of their customer or product master data. 

3-Collect, organize and analyze data

Once the data strategy is in place, the next step is to collect, organize and analyze data. Ideally, you want to collect and manage your data in a centralized repository. A data warehouse is a central repository for all of the organization's data; to store historical data and current and future data. The warehouse can support reporting, analysis, and decision-making. 

Collect and analyze data to understand what is happening in your business and identify any patterns. 

Gather accurate and timely data from all parts of the organization. This data must be cleansed and normalized for analysis. These activities can use automation and artificial intelligence tools such as machine learning algorithms.

The quality of data is essential to the success of any Data-Driven Transformation.

The accuracy, completeness, and timeliness of data are critical factors in determining data value. It isn't easy to make accurate decisions or take effective actions without high-quality data.

4-Evaluate your data skills and infrastructure

Identify existing gaps to support a data-driven transformation. Train or reskill your employees to use the new tools and technologies introduced as part of the transformation process.

5-Create models

Once you have analyzed the data, you need to create models that can help you predict outcomes or trends. You need to learn about the different types of automation and artificial intelligence and implement them in a data-driven transformation. 

Build or buy an artificial intelligence or machine learning platform. 

Work with experts in automation and artificial intelligence to get started on implementing a data-driven transformation in your business. 

6-Put the data into action

Engage with the data to make decisions and implement those decisions. One of the most critical aspects of this step is ensuring that the data is used effectively and efficiently. 

As  Chandra  Mostov, COO of Marketing, Automation, and Personalization at Wunderman Thompson, explains:

“ How do you fight the averages to actually get to understanding each of the data points on the consumer? How do you activate that unique data through algorithms and artificial intelligence? We all tend to think in averages, and that’s the opposite of what we should be doing.” (1)

You can answer these questions by creating a data-driven culture where everyone in the organization is focused on understanding and interpreting the data.

7-Monitor and evaluate results

The final step in a data-driven transformation is monitoring results. 

• Track the progress of the business against its goals to ensure the changes have the desired effect
• Implement changes and make necessary adjustments along the way based on insights gained from the data.
• Refine the models as needed, update the data warehouse with new data, modify the models as results change, and add new algorithms as required. 

8-Bonus step: Celebrate your success

Once you complete a data-driven transformation project, iterate on the next one.

A transformation is in constant motion.

One last thing

Get started small and focus on projects that will provide the quickest return on investment when embarking on a data-driven transformation. Projects need to be small in size to be manageable, but not too small, as the value delivered needs to be clearly identifiable and measurable. 

This approach allows you to test your hypotheses and prove the value of data-driven decision-making. It also allows you to build up momentum and create the required culture for your data-driven transformation.

For more data-driven insights on how you can digitally transform your organization, subscribe to Salesforce’s Vantage Point magazine: https://invite.salesforce.com/vantagepoint/ 

(1)https://www.salesforce.com/blog/data-success/

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