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When should a company be considered mature in the use of data?

How many times have we heard that most companies collect and use their data to make decisions? This could be considered the absolute minimum that companies must do to define a strategy that allows them to grow and expand their operations, but currently, the scope of what can be done with the data is greater.

A piece of data is not useful to us until we place it next to another and that data set provides us with information that is hidden at first glance. A number that expresses the amount of a sale does not generate much value if we do not put it in at least two dimensions, such as time and location. In this way, we can carry out an evaluation of the information and make a decision based on that number to know if there was a growth or a decrease compared to other data, be it internal or global.

In the industry, there are specific dimensions depending on the business in which the information is generated. Within the retail industry, in addition to the dimensions of time and geographical location, there are other dimensions, such as the organizational structure of companies, the structure of their products and/or services, the structure of suppliers and customers, to name a few.

This article proposes a series of basic steps that lead companies to enrich and mature their strategy for the use of information.

1. Prepare a Strategy for Obtaining the Data

Having identified all the points where the data originates is extremely important; they are the points from where the necessary information will be obtained to conduct the analysis. All the information is important: the data that originates in different systems where it is possible to originate a sale, the comments of customers about the brand and its products, and even data from the competition that can be added will be one of the most important points that must be considered as the strategic data for the following steps.

2. Obtain the Data in a Wide Universe of Information

All data is valuable but it also has an expiration date. It is important to capture most of the data related to the company and its products and/or services, so ‘receiving antennas’ must be implemented in as many points as possible where said data is generated: from the same point of sale, e-commerce, mobile applications, social networks, current events, and direct opinions of the clients. In this way, the capture and storage of as much data as possible is facilitated.

3. Evaluate the Quality of the Data

A piece of information is valuable as long as its dimensions are correct. It is important to adjust the price of a product at precisely the right moment. Otherwise, there is a risk of the cost surpassing the price. Assessing the quality of the data is important. Established metrics and indicators can help to visualize this within the processes of obtaining information. Wherever possible, implementing automatic quality assurance processes can alert us when an indicator falls below or above its statistical behavior. Not having these processes can create chaos in the organization since incorrect decisions can be made with the collected data.

4. Review Desired and Unwanted Behaviors

Once you have data with an adequate percentage of quality, it is time to find behavior patterns within it. Currently, this role within an organization is carried out by a ‘data scientist,’ who, through different tools such as Python or R, allows us to know the seasonal sales trend, customer behavior in a specific area, or affinity between similar products so that specific strategies can be designed to get the most out of the information.

  ​Consumers are increasingly interested in products that have a reduced carbon footprint, that are 100 percent recyclable, that can be purchased online, and that are of good value  

A well-developed strategy for the design of a product plan program is not a matter of aesthetics; it has to do with the level of sale of the product and with the products that are consistently sold together. For example, it may not be very logical to think that someone who buys milk or diapers at night might also buy beer. However, a parent leaving the office may receive a call saying, “we need milk and diapers…and can you also grab a beer and some snacks and a lime?” For this reason, a well-designed planogram is related to the purchasing behavior of customers, which is in constant movement, so it is important to frequently review the validity of data regarding buying behaviors of customers.

5. Implement Strategies to Influence the Data

The most important thing about data and information is to be able to influence it. Implementing a prompt, data-driven strategy is the result of preparing, obtaining, evaluating, and reviewing information. The importance of this activity requires a level of maturity from staff and from the organization itself.

In the interpretation of the data, we cannot go from the basic level, which is limited to seeing what happened yesterday, to a level where, through variable data that we can predict, such as the weather forecast or massive events, plans can be made to achieve a favorable result for the organization.

For example, faced with a forecasted heat wave, a company quickly activates an existing strategy to buy products such as water and soft drinks on a specific schedule, ensuring a better price through negotiation and thus offering attractive promotions to customers in their stores.

The strategy becomes an important differentiator for the company, achieving better revenue.

Conclusion  

Although most companies already track data, few have a data[1]based strategy to leverage the data and influence the behavior of customers. The strategy must have the necessary mechanisms to measure the result and apply necessary adjustments to improve it. Let's remember that customer buying behavior constantly changes. Consumers are increasingly interested in products that have a reduced carbon footprint, that are 100 percent recyclable, that can be purchased online, and that are of good value.

Implementing these five steps is of the utmost importance in organizations moving from descriptive analysis to predictive analysis using technologies such as big data, machine learning, and artificial intelligence. This acceleration must become an important pillar in the strategies of all organizations.

In an interview, Jose Rene Nuñez Sanroman, Business Intelligence Manager at Iconn discusses his thoughts on the evolving IT industry.