According to McKinsey, data-driven organizations are 23 times more likely to attract customers, six times more likely to retain customers, and 19 times more profitable.
Indeed, data and analytics do not change the business. But relying on data and analytics, leaders can make accurate and effective decisions that improve their business results.
As digital transformation becomes an inevitable trend in today's business environment, data plays a more critical role than ever. The more information leaders have about their company, customers, and products, the better they can strategically align with changing market needs and maintain their competitive edge. However, many organizations are too impatient to apply technology for digital transformation when not ready in terms of data.
So how should businesses prepare data to be ready for digital transformation? Let's go through 4 below steps with Abivin!
Step 1: Aggregate data
First, businesses should start collecting and aggregating data.
Currently, many businesses use dozens or even hundreds of separate applications and systems (e.g., ERP, CRM, etc.). As a result, data is easily fragmented, duplicated, and most commonly outdated when passed between departments and divisions. At that time, basic questions like “Which customers are bringing the most profit to the company?”, “Which products are generating the best profits?” becomes more complex to answer, or at least less precise.
Therefore, businesses should (1) identify the current data storage locations, (2) classify the existing data by name, data type, value, access, etc., and (3) systematically aggregate them.
Step 2: Clean data
After the data has been aggregated, businesses should start cleaning them. Specifically, the data can have many missing values, out-of-range values, null values, duplicate values, spaces, etc. Inaccurate data or errors may lead to errors in the business operations of the enterprise.
For example, when using Transportation Management Software (TMS) or Route Optimization Software, if the dispatcher enters the wrong address of the customer or the weight of the goods, the software will assign orders to unsuitable vehicles which do not match the actual weight of the goods. Suppose the organization does not promptly detect and fix the problem. In that case, the cost and delivery time will significantly increase, reducing the quality of customer service and affecting the business’s reputation.
Cleaning data is essential to avoid errors caused by using insufficient quality data, but it is also the most time-consuming step.
Here are some workarounds:
Trim off whitespaces.
Delete duplicate values.
Replace missing values (e.g., Insert default value, or look up additional information).
Standardize data formats (e.g., Format customer names, product names, data dates, or abbreviations consistently).
Normalize values (e.g., Convert all measurements to metric, convert prices to common currency).
Step 3: Store data
In the next step, the data needs to be put into the same storage system. When data sources increase, and the need to access accurate and timely information becomes urgent, businesses should turn to master data management for providing consistent and timely updated information to all departments in the company.
For example, when a business sells a brand new product to an existing customer, the salesperson can rely on the data stored in the system (customer information, old orders, etc.) to consult accordingly. Besides, suppose a customer closes an order. In that case, the dispatcher can take advantage of the customer's historical data about the delivery address, the number of orders, etc., to optimize the delivery route.
By storing data in a systematic and easily accessible way, businesses can enhance the customer experience and increase their engagement with the company, thereby generating more profits.
Step 4: Analyze data
In the process of digital transformation, data transformation is not only collecting, synthesizing, cleaning, and storing, but it is also about analyzing and updating continuously in real-time so that leaders can capture market and customer changes quickly. As a result, they can make timely adjustments to their digital transformation strategy to improve the customer experience and drive more profits for the business.
Businesses can feed data into advanced analytics tools such as descriptive analytics, diagnostic analytics, predictive analytics, or prescriptive analytics. To serve each specific need, companies should use the right analysis tools to get the most necessary and meaningful information.
For example, predictive analytics helps businesses predict future market trends and their impacts on the organization's business so that leaders can make preventive and reasonable risk reduction plans. If leaders want to solve the backlog (e.g., cut costs and lead time) to maximize value for the business, the prescriptive analysis will be the more practical choice.
Data is a business asset. With so much data being created and stored every day, businesses need to utilize it effectively to create more value for customers, businesses, and partners. However, not every organization can easily overcome the data challenge in the digital transformation process. Therefore, companies need to prepare carefully from collecting, synthesizing to analyzing, and managing data.