Find a passage from one of the articles that you find to be particularly meaningful or insightful. Copy the passage and expand with your thoughts on how it relates to the value of data or marketing analytics.https://www.forbes.com/sites/howardbaldwin/2015/03/23/drilling-into-the-value-of-data/#13eb42565fac
Explanation & Answer length: 150 Words each Reply
Student 1 Response: Choosing “Your Data is Worth More Than You Think” is the most relevant to my current situation so I would like to elaborate based on the article. Working in small business for almost a year compared to corporate has shown both the pros and cons of the two environments. A notable pro is the ability to make changes and see the impact either more directly, faster, or both. As my partner and I considered taking over the business, we have seen the importance of the data. For instance, my goal is to understand the business and ensure this is a major lifetime choice we want to make. Prior to this, I worked with a program, PowerBi in setting up the data sources to be analyzed by the marketing analytics while being urged to minor in business analytics. The “next big move” considered by one of my mentors.
He was right; now that we have all the data, how do we use it let alone value it especially in my new potential venture. I started with prior data of the typical business P&L, balance sheets, and cash flow; however, my incentive program would be made as a proposal on how I could improve the business until the time of the potential buy-out. Since, I have made our internal processes more efficient and effective, but am I hitting the right areas? Multiple regression could help me better understand where to make reasonable goals for myself and what is right for the company, improved commission plans for the technicians (in high demand), and where to market to retrieve my best-efforts finding loyalty customers (our annual preventive maintenance members). As told in the business evaluation, this is the only item that is worth 7 times the average spent on each plan! Simple regression to send out postcards to the right zip codes or other demographics.
Another aspect related to the topic was how detriment could be to the seller and high potential for the buyer’s favor. We just had a company eliminate their Heating and Air Condition (HVAC) area because they were not making enough compared to other places. What did they do with their people? They recommended us for zero! They decided it was “too hard” to know how to value their customer base and thought it would be better recommending a good quality place. From where I’m sitting, this is wonderful news and humbling, but astonished at the reason. Student 2 Response: Many organizations are keen to monetize data directly by selling it to third parties or marketing data products. Inability to understand data’s value can result in mispriced products. Understanding the impact of exposing data to third parties on the value of a company’s data for indirect monetization can help guide the decision on whether to pursue explicit monetization.
Today, despite an increasing recognition of potential benefit, most organizations are very conservative about what data they expose outside the enterprise. Good valuation approaches could help leaders understand if selling their data would really affect their competitive position or ability to realize their own benefit from it. Student 3 Response: COMPETING IN A DIGITAL FIRST WORLD Capitalizing on insights derived from data, users across an enterprise can make better decisions, evaluate risk, and find ways to engage and keep customers. While this information is certainly valuable, what is the exact value of enterprise data? Many CIOs and C-suite executives have been asking this question. In our experience working with Fortune 500 companies, we recommend a structured approach to put a value on enterprise data.
It is built on three layers: intrinsic value, derivative value, and algorithmic value. I think that the takeaway for me is that there isn’t a one-size-fits-all approach to valuing data. Different industries not only have access to and collect different types of data about customer behavior, but they also use the data differently, so it makes sense that the value of data should be made up of aggregating different approaches. While the data has value, what the business chooses to do with it can increase or diminish that value, particularly considering the quality of the data. If the quality of the data is poor, not only can it reduce the value, but it can possibly cause the company to lose revenue when initiatives fail as a result of build faulty models based on incomplete, inaccurate, duplicated, or untimely data. Part of analyzing the data to build a model should include an assessment of the quality of the data before the modeling begins. Not knowing how to analyze or model the data effectively can lead to over-valuing and under-valuing the data in any of the intrinsic, derivative, or algorithmic layers.
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