Question

• Why do you believe data can be inaccurate?
• What can a business do to ensure that data is correct?
• Explain how bad data will impact information, business intelligence, and knowledge.
• Argue for or against the following statement: “It is better to make a business decision with bad data than with no data.”

Answer 1

The opposite of inadequate tracking is over-tracking (or over-inflating). Obviously, this can be counterproductive to research, because it could show the digital assets, campaigns and content to be performing very well when this is not the case in fact. It also has a serious effect on main indicators like bounce rate (will fall to 0 percent) and could theoretically double 'tracked' orders/revenue. As organizations become more sophisticated with their analysis of web analytics, the need emerges to track beyond the simple view of the website / computer. Many analytics systems provide a way to monitor user experiences or special attributes that can occur about the users, sessions, pages or experiences. If you are an ecommerce site, such as product descriptions and features, some examples may be unique user attributes for your identified or logged in customers (such as user ID) or main product attributes;

A typical step for any company would be to take data from a variety of sources. Most firms tend to tap on the same reserves though. Through looking outside of the standard at data wells, you will also gain new knowledge and verify the details you already have in effect. The forex market for example can be a strong predictor of spending habits

The thing to remember is that not all data are homogeneous. Most inexperienced data analysts think you can apply one set of data to all circumstances. It really isn't real. The quality of the data you have may differ between source and source. In other words, the accuracy can vary depending on how the data set was gathered, cleaned, and processed. But a lot of people don't get that.

Instead than treating each data source as its own individual, business owners are lumping them together and treating it as if it were a vast set of infallible data. The truth is that all sources need to be individually measured, and then viewed as a whole. As we pointed out in the first paragraph, observations from various sources need to be cross-referenced, concepts extrapolated and then a rational conclusion reached. You can only ensure the observations are correct by doing so.

Although individual treatment of data sources is important, it is also important to provide a common repository for collection. The question can be compounded by data silos that collect knowledge from various departments. To put it another way, the storage on various systems of data from various departments can lead to inaccuracies. Yeah, every single source has its own meaning. But you do need a way to link it to create a complete image. The only way to efficiently do this is by using a central forum. It's much easier to link the dots and produce more accurate outcomes by combining data and removing silos.

As the customer path becomes ever more nuanced, there are more opportunities for a customer to be introduced to the brand than ever before. When you are unable to reliably monitor which channels or promotions drive conversions, you lose visibility in insights into efficiency, potential sources of revenue, and the ability to better determine why conversions are happening or are not happening. Missing data , for example, may underestimate certain channels that may actually drive more conversions at a lower cost. Without an insight into the entire data set, however, you can shift budget to underperforming or more expensive channels, limiting the sales potential and increasing media spending.

Simply put, a lack of data leads to more caution than poor data. When you don't have enough data to act on it, there is a possibility you 're going to be careful simply because you think you're going into a blind situation. But, if you have poor results, you will always find that you run straight into a bad option with reckless abandon. Bad data has the horrible potential to allow your company to do the wrong thing with full certainty that it makes the right choice. This means investing resources where they don't have to go and making the kinds of choices that would be ideal – at least if fact suited the reports to what you have. If you have only bad data to go on, then you can only make decisions that hurt your business.