To make sound and profitable decisions for your business, you need to have accurate and timely data at your fingertips. Most entrepreneurs trust their gut feeling when evaluating and improving their company. This means that topics like strategic planning, strategy formation, daily operations and important decisions are only based on their experience and feelings.
Data-driven strategic management (DDSM) is a business strategy that relies on this data to make informed decisions. In this article, we will discuss what DDSM is, how it works, and why it’s such an essential tool for businesses today to make the right decisions and become future-proof.
What is data-driven strategic management (DDSM)?
Data-driven strategic management (DDSM) is the process of making decisions about a company’s future direction using data analysis. This involves gathering data from a variety of sources, including internal data (sales data, employee data, etc.), external data (market research, competitor analysis, etc.), and Big Data (data gathered from social media, sensors, etc.). Once the data is collected and analyzed, it can be used to make informed decisions about product development, marketing strategy, and resource allocation within the organization itself.
One of the advantages of using DDSM is that it makes it possible to see the big picture of a company in a way that would be impossible without the necessary data. By analyzing data from all areas of the business, decision-makers can get a more holistic view of what’s happening inside and outside the company. This can help them make better strategic decisions based on an understanding of the full context.
DDSM is becoming increasingly popular due to the growth of Big Data and advanced analytics technology. As more and more data is collected, it becomes increasingly crucial for businesses to have systems in place to manage and analyze it. Another option is to use external data providers and business analytics platforms (e.g. MoreThanDigital Insights) that can help organizations to see a third-party picture of the company. This can be important as internally collected data might be biased or just show part of the story.
There are a few things to keep in mind when using DDSM. First, it’s important to have clear goals and objectives for the analysis. Without a clear idea of what you’re trying to achieve, it will be difficult to properly interpret and act on the data. Second, it’s important to have access to clean, accurate data. If the data is of poor quality, it will be difficult to get reliable insights from it. Finally, DDSM is not a silver bullet – it’s just one tool that can be used to make better decisions. It’s important to use other methods (e.g. market research, competitor analysis).
To make the most of data-driven strategic management (DDSM), it’s important to have a data-driven culture in place. This means that everyone in the company is comfortable working with data and using it to make decisions. The managers need to be able to interpret and use data effectively, and the employees need to be able to provide reliable data sources.
A data-driven culture is important because it enables everyone in the company to make better decisions. By having access to accurate data, managers can see how their decisions are affecting the business and make course corrections as needed. Employees can also use data to improve their performance by tracking their own results and benchmarking themselves against competitors.
Creating a data-driven culture can be challenging, but there are a few things that can help:
- Managers should lead by example and use data to make decisions whenever possible.
- Employees should be given access to accurate, timely data so they can track their performance and contribute ideas for improvement.
- Relevant data and insights should be displayed in tools the employees and managers use every day
- Training and education on how to use data should be made available to everyone in the company.
- The company should establish clear goals and objectives for using data. It is important to communicate this so everyone is on the same page.
- The company should foster a culture of experimentation so employees feel comfortable trying new things and using data to measure the results.
Data as Basis for the Company Strategy
Boards of directors, C-level executives, and other senior leaders need accurate data to make informed decisions about the long-term future of their companies. Board meetings and C-level meetings provide a forum for discussing strategic initiatives, but these discussions can be enhanced with the use of data.
Board members and senior leaders can use data to identify trends and opportunities in the market, assess the competitive landscape, and make informed decisions about where to allocate resources. Data can also help companies measure the effectiveness of their strategies and track progress toward their goals.
By using data as a foundation for their decision-making, boards of directors, C-level executives, and managers can ensure that their company’s strategy is based on reality rather than guesswork. When managers have access to all the relevant data, they can make better decisions faster. This can help them avoid costly missteps and position their company for success in the long term.
Internal vs. External Data for DDSM
There are two main schools of thought when it comes to using data for strategic management: internally-generated data and externally-sourced data.
Internally-generated data is data that is collected by the organization itself, typically through its own systems and databases. This data can be used to track and analyze the organization’s own performance, as well as to make comparisons against competitors or industry benchmarks.
Externally-sourced data, on the other hand, is data that is collected by outside organizations, such as market research firms or rating agencies. This data can be used to track the performance of the organization relative to its competitors or benchmark its performance against industry averages.
Internal Data for Data-Driven Strategic Management (DDSM)
Examples of internal data for data-driven management include:
- Sales data, including revenue, unit sales, and average selling price
- Cost data, including material costs, labor costs, and overhead costs
- Product data, including product lines and SKUs
- Customer data, including customer counts, customer demographics, and purchase history
- Location data, including store locations and distribution centers
Advantages and disadvantages of using internally-generated data?
- Can be more accurate than externally-sourced data, because it is collected directly by the organization itself
- Can be more timely than externally-sourced data, because it is not reliant on third-party data collection timelines
- Can be more specific to the organization’s own products, services, and customers than externally-sourced data
- May be biased if the organization is not careful about how it collects and analyzes the data
- Maybe less comprehensive than externally-sourced data, because it does not include data from other organizations
- May be more expensive to collect and maintain than externally-sourced data, because the organization must invest in its data collection infrastructure
External Data for Data-Driven Strategic Management (DDSM)
Examples of external data for data-driven management include:
- Market share data, including market share by a competitor and by product line
- Industry sales data, including industry sales growth and total industry sales
- Customer satisfaction data, including customer surveys and Net Promoter Score (NPS)
- Employee satisfaction data, including employee surveys and engagement scores
- Press coverage data, including positive and negative sentiment analysis of media articles
Advantages and disadvantages of using externally-sourced data
- Can be more objective than internally-generated data, because it is collected by third-party organizations that are not affiliated with the organization being analyzed
- Can be more comprehensive than internally-generated data, because it includes data from other organizations in addition to the organization being analyzed
- Can be less expensive to collect and maintain than internally-generated data, because the organization does not have to invest in its data collection infrastructure
- May be less accurate than internally-generated data, because it is collected by third-party organizations that may not have the same standards for accuracy as the organization itself
- May be delayed compared to internally-generated data, because it is reliant on third-party data collection timelines
- Maybe less specific to the organization’s products, services, and customers than internally-generated data
Challenges of a Data-Driven Strategic Management (DDSM)
The challenges of data-driven strategic management are twofold. First, data can be misleading, and second, data-driven culture and training are necessary for success.
Data can be misleading because it can be filtered or manipulated to support a particular viewpoint. For data to be truly useful in guiding strategic decision-making, it must be accurate and unbiased. Education and “data literacy” are essential for discerning the true value of data.
A data-driven culture is necessary for success because it enables an organization to make decisions based on empirically-validated evidence rather than intuition or guesswork. A data-driven culture also enables organizations to rapidly adapt to changes in the environment. Training employees on how to use data effectively is essential for developing a data-driven culture.
Conclusion – DDSM for Businesses
In a fast-moving business world, it is more important than ever for companies to use data to get the full picture and make informed decisions. External data can be helpful in this process, but it is critical that companies also rely on internal data generated by their employees. The challenges of data-driven strategic management are twofold: first, data can be misleading, and second, data-driven culture and training are necessary for success. By overcoming these challenges, companies can make better decisions based on empirically-validated evidence rather than intuition or guesswork.