When analyzed correctly, data can be a powerful tool to drive business. Such power can be addictive too. However, there are both positives and negatives to using data science in order to enhance business efficiency. Traditionally, most business decisions were based on past experiences or instincts. Applying wisdom and experiences in some situations is quicker and beneficial instead of analyzing massive spreadsheets and dashboard.
“Mistakes are the portals of discovery.”- James Joyce (famous Irish novelist).
The job of a data scientist is similar to that of a detective – discovering the unknown. However, in a quest of discovering the descended data, they do tend to fall into the pitfalls. When it comes to data science mistakes, as majority of today’s business decisions are based on data, data scientists have a very small margin for error. Herein we help you to understand the most common mistakes in data predictions and ways to avoid them.
- Piling-up messy data: The upshot of big data depends on the quality of data that’s being analyzed. Start with an assumption that your data is inaccurate. When you start working with a skeptical outlook, you might come across duplicate records or inconsistent spelling errors. Keep a check using look-up tables or speedy analytical tools and filter textual information through language correction libraries to polish the content. Make sure you have quality data to generate accurate results.
- Not adjusting for seasonality: Business and economic data acquired at the time of holidays, summer months, or other seasonal times of the year, can be messy and misleading. Let’s say your business is up by 75% in the month of December when compared to November. Does that statistics imply that you’re doing good? Or is it just normal for businesses to do well in Christmas, or maybe sales are usually up by 100% and you’re missing out on something here? Even a 3-month trend can be excused owing to the back-to-school time or busy tax season. Always consider any such seasonality while analyzing your data: even particular hours of the day or days of the week matter. Seasonal adjustment in your data science practices doesn’t cost anything except some time and bother.
- Ignoring outliers: Dealing with the outliers can be complicated when conducting an analysis of your website data. Most of your numeric datasets are going to contain outliers because some special exceptions or some miscalculated wrong data. Outliers can ruin everything, they’re evil! You must get rid of them before making any predictions as it may affect the result of the mean calculation.
- Fixating on outliers: There’ll be situations with extremities, like a huge spike or drop in website visitors or lead volume. Many a times these fluctuations are just fluke, however, these outliners can also indicate that something is wrong with your system – a broken process or unresponsive web form. One must investigate these outliners in the data to make sure everything’s in-line with the process. Do not fixate your decision based on a mis-read outliner, unless you have other data elements that confirm your audiences’ need. Try to predict customarily distributed data, instead of Cauchy-distributed.
- Not watching metrics in context: In your early days of data analysis, you might find it tempting to focus on small wins. While this is definitely important and a great motivational element, make sure you’re not distracted from other principally important metrics; such as, sales and customer satisfaction.
- Data overload and chart junk: One critical source of confusion is just too many metrics. Sometimes the golden advice of ‘less is more’ should be taken into consideration. Especially while making a dashboard or just a simple analysis. Always make sure that everything on your screen has a clear goal and there’s no extra elements of distraction.
- Data Lifecycle: Timing plays a key role in relative analytics, but many estimates go awfully wrong if they don’t consider data lifecycle. For example, if you introduced a product in April 2018 and there are no sell-I parameters for the first quarter of the year. Your import production for Q1 2019 might be terrifyingly low. It may leave you perplexed into adding more indicators to the research for a better and more accurate outcome. However, data lifecycle awareness could alleviate your process and lead you to a completely different result.
- Authenticity of your resource: Most of your data is piled-up in different formats, as it is collected from different systems within your business. For instance, your website visits come from your web analytics tool, your email list is managed by another provider, and your customer list is obtained from many other different systems. When you collect and combine such data from different sources, ensure that you haven’t made any mistakes in the process.
- Contemplating noise instead of signals: Our brain is genetically compelled to spot trends. Sometimes we observe rewarding patterns at hopeless places. Be sure you’re not stuck into a process which is feeding you false information based on your desperate need to see signals.
- Not evaluating the results of a decision. It’s the most common mistake to just assume that your results are correct. However, making decisions for your business might require wisdom beyond your capabilities. It’s human to make bad decisions, everyone ends-up doing so. But, if you refuse to accept and correct your mistakes, you might end up in despair. Also, if you are methodical about your decision-making process, there might be a better collateral that will lead you to success. No matter how uncomfortable it is to make a hard call, it’s important to evaluate your results and make sure that you made the right call.
Big data has tremendous potential to push your business beyond the horizon, but it can also ruin your frail chances if you make false predictions. Data analysis does not have to be a pounding task needed to be performed before initiating work. Instead, it should be used to recognize trends you would have otherwise missed-out. Tracking key metrics of your business is a great way to track it’s wellbeing. Also, it’s important to keep the big picture in mind when digging into your data but ignoring smaller aspects can be harmful to your decision. Decisions should be informed by your business data, not driven by it.