Why are organizations investing so heavily in data collection? Once data is collected, what needs to be done in order to turn it into something valuable? In this lesson, we'll answer these questions by exploring how companies use the data they collect, and the various reasons an organization may have for committing time and resources to data collection and data organization. While there are many ways to organize data for analysis to differ from industry to industry, there are two main objectives. Understanding and monitoring business operations and gaining analytic insights to drive business decisions. We'll explore data use cases in reference to both of these objectives. Operational analytics are used to understand and monitor business operations. A common example is the use of a company's financial data to measure and report on the company's operational health. In this case, the data that needs to be collected may include all the details about how the company makes money, and what expenses it incurs throughout its operations. The organization's finance team, who monitors and assures the company's successful ongoing operations, would be responsible for the collection and organization of data for this particular use case. At any point in time, the finance team needs the appropriate data to report how much revenue is coming into the business. Revenue data needs to be collected and organized to understand the company's daily income, as well as income rolled up weekly, monthly, and yearly to report on the trend of the company's financial health. Also, the team would need the data to understand how to break down and organize revenue numbers by all relevant categories. Those categories can include the types of products sold, the locations where revenue is generated, the types of customer producing that revenue, the business units responsible, and other data points. The revenue source itself can be quite complicated, coming from customer purchase transactions, purchase orders, third-party grants, investment returns, and other channels. Data from all these different areas needs to be collected and organized in order to have a complete view of the company's finances. The larger the organization, the more complex its financial data and operational analytics requirements will be. The goal of operational analytics is to produce and organize data so that internal customers, such as managers, executives, board members, and auditors, can understand different aspects of the company's operational health from any point of time. Operational analytics are meant to be objective and accurate representations so that the consumer of those analytics can continuously monitor operational activities and possibly choose to take action based on the available data. The second use of collected data is to leverage it for analytics insights. Rather than trying to provide ongoing information about business operations and processes, the goal of using data for analytics insights is to help drive specific decisions. An example is the use of data by an organization's sales and marketing teams to identify and target their ideal customers. In this scenario, the marketing team is responsible for collecting and organizing data in order to optimize companies spending and marketing and sales to acquire more customers. The insights generated by this type of data analytics are valuable because they tie directly to company revenue. However, the creation of marketing insights may be challenging because all the data needed may not be readily available and decisions based on bad data can lead to poor or even disastrous outcomes. Another challenge when using data for analytics insights is that the actual value of the data is sometimes impossible to determine until the outcome of the database decisions is known. For example, let's say a local bakery that sells cakes wants to know what types of new cakes they should produce to best grow the business. In order to collect data, they commission a marketing agency to provide research data about trendy recipes around the world. They also survey their neighborhood to better understand where people shop and the type of cakes they enjoy. In this scenario, how valuable is the data from the marketing agency? How valuable is the neighborhood survey? Well, it's difficult to answer. In order to measure the return on investment from the time and expense spent on collecting that data, we have to take action on those insights to see how and if the business grows. Maybe the results are incredibly favorable and the business grows 50% by designing a new offering of cakes recommended by survey respondents. On the other hand, the results might be poor if the time and money spent on the marketing agency's research outweigh any new business brought in by the trendy new cakes. Or the marketing data could be worthless if the bakery simply decides to do nothing and make no new changes. When organizing data for operational use, the results are usually defined and expected, whereas organizing data for analytics insights can end up with unexpected or variable results. On the other hand, analytics insights can support high impact decisions at a strategic level, while operational data may simply assure that the business is running as expected and doesn't ultimately impact the growth of the business. These different aspects of data for operational use and analytics insights play a role in how organizations value their data and how they approach collecting and organizing data. But it is possible to use the same data set for both operational use and to drive analytics insights. For example, let's look at some operational data for manufacturer supply chain. In order to monitor and understand how they are doing, they must collect and organize data about their suppliers, inventory purchases, inventory costs, and inventory on hand, among other data points. From an operational standpoint, they need this data to keep the supply chain operation going. By constantly ensuring that for now and the immediate future, suppliers are providing inventory, the company is purchasing the right inventory items, the inventory costs are affordable, and the company has available inventory. With this same set of data, how can we now drive analytics insights? The insights that can be driven from this data set would depend on the type of questions being asked. For example, let's say the sales team is looking for business opportunities in other markets to expand the company's reach. While the company is open to investing in the expansion, they need insights into how to penetrate new regions efficiently. In this scenario, the company's data on their current supplier and partner relationships can be incredibly valuable. By looking at the data around the third party suppliers they already have information on, as well as the amount of money they spend with those suppliers, they can drive insights into potential partners in different regions. Now that we have a better grasp of how data can be used for the purposes of operational analytics versus analytical insights, we should be able to better understand what a particular set of data can potentially be used for.
