Relationships between businesses and their data are transforming. By embracing an end-to-end mindset, organizations will be able to transform data into business value to a greater extent.
By combining the front-line team’s data with their day-to-day interactions with customers, and then translating back into the company’s internal network, an end-to-end data culture is created.
We’ve identified five key challenges that organizations face when trying to establish a data-driven culture based on our experience working in military intelligence and human-centered design, as well as the insights of over 1,000 business leaders.
The Extent of Data-Driven Decision Making
Data-driven decision-making is the process of collecting and analyzing data, inferring insight from it, and then making decisions based on that insight.
The process is objective and can be analyzed according to the impact of the metrics on the data.
Every manufacturing industry can benefit from data-driven decision-making. To save time, management can plan what will speed up the production.
Benefits of Data-Driven Decision Making
Enhanced strategic agility
Data, such as sales figures, material costs, and market projections, have always been used by businesses to formulate their business strategies. Those strategies are largely influenced by the variety, velocity, and volume of data available to modern businesses.
Data-driven businesses must become adept at acquiring, analyzing, and acting on new data quickly due to the increased adoption of emerging technologies and applications that require low-latency access to large volumes of data.
Improved customer visibility
An enterprise that uses data smartly knows its customers better than ever. You can learn where your customers come from, what their needs are, what they want to buy, how they want to buy it, and how they want to contact you.
Data collection isn’t the secret to knowing your customers. It’s about having the ability to unify data from multiple sources, and then making it accessible, actionable, and understandable to the people in your organization who need it most. The sophistication and complexity of this kind of analysis require next-generation network infrastructure.
Innovation-driven by insight
A data-driven business with a full understanding of its customers can use those insights to improve its applications, offers, and experiences for its customers.
The retail sector, for example, has been able to envision what customers want from their in-store experiences using customer data.
Retailers are developing the store of the future through continuous innovation, offering lessons for other industries too.
A successful business is built on happy customers. Many companies ignore the fact that behind-the-scenes improvements may be the most efficient way to improve customer experiences.
Businesses can optimize their operations in real-time by monitoring their data intelligently. Businesses can track and automatically adjust processes and operations to respond to disruptions and demands by collecting data on equipment conditions, shipping routes, weather patterns, supply chain health, inventory.
Capital insights in real-time
Most businesses base their maintenance and capital expenditures on guesswork, not data. Guesses about when to service or replace a machine are based on estimates.
A more challenging situation is when equipment failure leads to maintenance and purchase decisions that result in lost productivity and unexpected expenditures.
You’ve probably experimented with hybrid and multi-cloud platforms and big-data exploration through your business. Next, expand on your business’ success so that it can become more competitive.
5 Challenges that Get in the Way of Becoming Data-Driven
Quality of data
The first data-driven challenge is in a data-driven project, data discovery can be a crucial and fundamental task. Depending on the criteria, such as user-centered frameworks and other organization frameworks, one can discover the approaches for data quality.
In addition to the methods of data profiling and data exploration, the analyzers will also be able to check the implications of their use as well as the quality of the datasets. It is essential to follow the data quality cycle to improve and ensure high data quality.
Data integration is the process of combining data from different sources and storing it together to obtain a unified view. Data integration problems are likely to be caused by inconsistent data within an organization.
To solve complex data integration issues, several data integration platforms are available. With data integration tools, you can automate and orchestrate transformations, create extensible frameworks, optimize query performance automatically, etc.
The third and most important data-driven challenge is called dirty data when it contains inaccurate information. Taking it out of a dataset is practically impossible. It is necessary to implement B2B Data-Driven Marketing Strategies to work with dirty data based on the severity of the errors. The types of dirty data are listed below.
- Inaccurate: Technically correct data can be inaccurate for the organization in this case.
- Incorrect: A field’s value must be within the valid range of values for it to be considered incorrect.
- Duplicate: The occurrence of duplicate data may be the result of repeated submissions, incorrect data joining, etc.
- Inconsistent: Inconsistent data is often caused by redundant data.
- Incomplete: Data with missing values is the reason for this.
- Business rule violation: A business rule is violated when this type of data is present.
Data management experts can help organizations overcome this challenge by cleansing, validating, replacing, and deleting raw and unstructured data. Also available on the market are data cleaning tools or data scrubbing tools for cleaning dirty data.
Uncertainty of data
Uncertainty can occur for many reasons, including measurement errors, processing errors, etc. When using real-world data, error, and uncertainty should be expected.
Simulating, testing, and analyzing complex systems can be simplified using powerful uncertainty quantification and analytics software tools.
Transforming data is the last data-driven challenge from multiple sources that are usually incompatible with each other and therefore need to be cleaned and normalized before they can be used together. To gain meaningful insights from data, Data Transformation can be described as converting data from one format to another. Even though the entire data can be converted into a usable form, there remain a few things that can go wrong with the ETL project such as an increase in data velocity, time spent on fixing broken data connections, etc.
Different ETL tools can be used to extract data and store it in the proper format for analysis, including Ketl, Jedox, and so on.
Having deeply understood these challenges, we have developed the framework to enable business teams to communicate with data when, where, and how they need to.
Team members need to be able to think holistically and make decisions with flatter structures to achieve this goal. By doing this right, you’ll be able to turn your organization’s data into real business value every day.