Data in Business

By: Parth Pahuja

“You can have data without information, but you cannot have information without data.” — Daniel Keys Moran

As technology advances so do our society, digital worlds are being formed all around our digital transactions which are becoming more prevalent than ever. Through this, a world of data generation is created. Startups are one of the niches that use this data the most, and although there may be an abundance of data, many newly-conceived ventures have difficulty leveraging value from the data they hold. In this article, I will be exploring how real results can be produced from vast varieties of data.

What is Data in Business?

It is key to understand the difference between data and information. Data is the raw numbers and facts, while information is data which is specific and organized. A way to think of this is that data is a sugar cane and information is the sugar cane processed into white sugar and allotted to a certain dish. In our situation the dish is the specific process or task we want to leverage the information for. Data in business is information related to the business’ operations. This includes statistics, analytical data, customer feedback, sales, and other information. Businesses collect as much data as possible on their operations to be able to utilize it to streamline their operations, in turn creating a more leaner and more efficient process within their systems.

Why is Data Important?

In essence, data is composed of statistics or facts that are collected throughout the use of a businesses’ products. These statistics and facts can be used to create meaning and to report factors like performance, outreach, and expenses which can prove to be integral to the development of a business. For example, customer data is a metric which helps businesses better understand their relation with their clients by providing data about customer interaction such as expenses, income, and pain points. Data helps businesses do things like:

• Make Decisions
• Improve Customer Satisfaction and Interaction
• Increase Revenue and Profits
• Help with Problem-Solving
• Improve Company Processes

For example, you might notice seeing ads of items that you have searched up in the past on sites like Amazon. This is an example of utilizing data to improve customer interaction.

Creating Transparency of Data Sources and Streams

Before working with any data it is important to know the source of the data and that the data is trustworthy and accurate so that we can make sure we know who to hold accountable if the data turns out to be inaccurate at a later date. Transparency helps consumers gain confidence in the service or product you are providing them while giving marketers assurance of the data that they are selling. Here is are some validators you can use to make sure that the data you are using is transparent:

• Create and maintain user-friendly procedures which allow people to manage the processing of their data.
• Clearly state your efforts to be within compliance and to be transparent to your users.
• Inform your users about the purpose for which you are holding their data, your intentions for transferring their data to other parties, and how you are processing their data.
• Use systems that hold data only as long as it is necessary.

There are two key factors of data transparency. The first factor is identifying the source of the data, its use policies, and its accuracy. The second factor is maintaining transparency in the communication lines in which it flows, meaning that the concept, numbers, and meaning of the data should not be changing as it gets passed down.

An example of this is shown within Slack’s privacy policy in which they mention that they “collect different kinds of information. Some of it is personally identifiable and some is non-identifying or aggregated. Here are the types of information we collect or receive…” Within this statement Slack is effectively creating transparency of their data sources by providing a clear understanding of how they are using their customer’s data.

Embracing Lean Startup Analytics

While holding the above factors in mind it is important that one keeps the analytics of the startup lean and manageable. It is possible to do this by getting rid of unnecessary data related distractions and finding a balance point where you are collecting just the right amount of data.

By getting rid of these unnecessary distractions I mean that you need to define a task that you are trying to accomplish and remove all the other data which will not help you accomplish that goal. This can be accomplished through data cleaning which is the process of identifying, removing, or fixing unreliable and inaccurate data from your database.

The Data Analysis Process

In this section we are going to go through the significant steps in data analysis. This consists of clearly defining a goal, gathering information, converting the information into data (see What is Data in Business?).

Defining a Goal:

Defining your goal is the first step in the analysis process. You will be asking yourself what it is that you are trying to solve. In essence you will be creating a hypothesis. Ask yourself:

• What is the demographic I am targeting?
• Is the question conditional upon parameters which can be calculated?
• Or is the question analytical, being a little more conceptual dependent on parameters which don’t have a clear cut numerical value
• Are the resources available for me to collect this data?

Say for example Company A needs to understand why their client retention rate is decreasing. The question they would formulate is “Within the months November to February why has the client retention rate decreased by 6% when providing X service?” This question targets a demographic which is the clients that are being sold the service. The question is conditional upon the parameters of the client retention rate and the resources are available within the data collected about the client retention rate. This is an example of a properly defined goal.

Gathering Information

Once you have recognized your goal you need to understand how and where you will gather your data. There are different types of data which consist of:

• First Party Data: This is data that a company will collect directly from its customers. This type of data can help narrow down the type of data needed, in turn making the process much more efficient. An example of first party data that Company A can collect is data from a survey asking former clients why they stopped using Service X.
• Second Party Data: This is the first party data of other firms. This data can be acquired from an organization or online campaigns and mainly hold the benefit of being more relevant, reliable, and formatted as it is more filtered. This type of data can be found on apps, websites, and activity or trends on social media. An example of this would be Company A using social media trends provided by a social media platform about their service to better understand why their customer retention rate is declining.
• Third Party Data: This is data which has been grouped and collected from many sources and is often referred to as big data due to its extensive size. It is often unstructured and is mostly leveraged to do market research. This data doesn’t have a direct relationship with the clients it is obtained from. An example of third party data is a public consensus which conveys trends about a specific topic.

Once you’ve understood the type of data you will be collecting and have conceived a data strategy you will need to start using tools to collect the data. Data management platforms (DMPs) are one of the most impactful tools you can use to pinpoint and identify data. DMPs can help your organization convert real time data into more structured and segmented data, further helping you in formatting and tidying up. DMPs such as Adobe Audience Manager, Google Marketing Platform, and MongoDB are some of the most recognized platforms today.

Converting the Data into Information

Now that you have collected your data you will need to filter and clean the data, therefore converting it into information. The significant steps in this process consist of:

• Removing Duplicates, Outliers, and Errors: Even though this seems simple it is almost inevitable that you may have small tedious issues like these that once deleted will make it much easier for you to navigate the data.
• Removing Irrelevant Data: It is only beneficial to go back through and remove any irrelevant data. Even though you made sure you had relevant data when gathering your information it is only beneficial to you that the data you are moving forward with is relevant and useful.
• Making the Data Neater: This includes fixing typos, creating formats and structures, and closing gaps between data. Within this process you are making the data more organized and ready to be analyzed.

Once you have tidied up the data you are using its time to finally convert it into information. This is called data processing. Data processing includes:

• Employing tools to analyze the data: You will need to utilize tools and technologies which will help you understand and extract information from the data you are using. Throughout this process you will be revealing trends, correlations, and patterns which use methods like different regression models and K-means clustering which delve into deeper analytical topics like vector quantization and signal processing. Ultimately you should be seeing movements and trends which start to answer the hypothesis created earlier.
• Transforming the Data into Information: Now that you have analyzed and processed the data you need to organize it into a relevant and accurate format. This means that you need to make it suitable for presentation. You can further accomplish this by implementing business strategies and ultimately reach a conclusion about why certain movements are happening within your organization.

Conclusion

Throughout all these steps and processes you can see that it is mainly an organizational and analytical task when it comes to converting raw data into useful information. Combined with business strategies this information can be very effective in creating change.