You know what you don’t want to mess up?
Aside from making sure you wear actual clothes to work or forgetting to feed the dog, you really don’t want to mess up your data.
Mistakes occur not so much in the analytical phase, but when preparing data for analysis. Data preparation is a far more time consuming task, even with small sets of data, than the actual reviewing and analyzing portion of the process. What’s worse is that there’s more than a fair share of space for human error to rear its ugly head when manually inputting data or formulas to analyze. It is the final analysis that’s easy and even, dare-we-say, fun?! It’s the data preparation that’s most challenging and that often takes the most time.
At Cyfe, we’re all about good, quality data made, viewed, and understood easily. So we’re here to help pinpoint some of the more common mistakes made when preparing data for analysis so that you may avoid these pitfalls and focus on the good stuff — data that’s accurate!
1. Preparing data for analysis without a clear goal.
Access to all the data in the world is essentially useless without a direct goal in mind. Sure it might be interesting to peruse heaps of big data, but for the sake business growth and innovation, clearly defined goals will help streamline the analysis. We recommend having those goals agreed upon in writing to ensure you and your team stay on track when preparing your data for analysis. If clearly defined goals seem too big an ask, then at least have clearly defined questions that you’re setting out to answer. This more organized and prepared approach to data analysis will save time and prove far more efficient than attempting to solve for mysteries. Be sure you and your data analysis team are on the same page so that you carry out your work in consistent strides that culminate in answers to your original questions and /or meet the goals you set out to achieve.
2. Deprioritizing data visualization.
When preparing data for analysis it’s incredibly easy to get caught up in the numbers without a single thought given to final presentation or even to data analysis review. Presentation or visualization matters because this is how you, your team, and others will view and interpret the data. Choosing your favorite pie or bar chart is all well and good, but if you followed our advice in #1 your clearly defined goals and/or questions should help inform the way in which your data ought to be visually recorded and presented. The wrong type of visualization may result in a skewed perception of the data or even eliminate certain questions from being asked, let alone answered. It’s time to decide on the best visualizations when preparing data for analysis, not when presenting your findings.
3. Ignoring issues outside the scope of data.
Remember that on occasion other measures well beyond the scope of numbers and digital performance can come into play in the world of data. It may be all too easy to allow data to inform every business decision, leaving gut instinct and issues of ethics trailing behind in the dust. While data can and should be used in important business decision-making, it’s never a good idea to solely rely on pure numbers without at least considering other outside factors. When preparing data for analysis it might be best to think of data as a motivator or influencer without final decision-making power. There may be ethical, cultural, or philosophical issues at play that may take precedence over pure data analysis. Be sensitive to these potential pain points to best understand how they may influence your final results.
4. Inputting the wrong data.
Yes, it’s possible that even you can make a mistake in basic data entry. Entering or merging information in the wrong row or column or adding an accidental zero at the end of a number are all incredibly common human errors when preparing data for analysis.
Our recommendation? More coffee! Just kidding. While coffee might certainly help, so can data input automation. When it comes to data analysis, any process that minimizes the risk of human error is a huge positive. Always look for ways to boost efficiencies and save time.
5. Analyzing a (too) small population.
While there’s of course nothing at all inherently wrong with working up analytics for a small population, it’s important to be prepared that the data may not present as much useful information as it would if your population were larger. Smaller populations tend to produce more outliers without enough correlations to discern what’s really going on. It may be best to wait until there is a larger data set or if you can at least view smaller data over longer periods of time. It’s often helpful to prepare population data for analysis when looking at specific timeframes, for example, year-over-year or month-over-month for more clear comparisons.
6. Mismatching or confusing naming standards.
Up front, organization and consistency are key when preparing data for analysis. If your naming conventions are even slightly askew, your data is in potentially huge trouble. Be sure to set up a simplified naming convention system before diving deep into analysis. Use terms that are clear and that will make sense to those with whom you plan to share your analysis. Make sure everyone is aware of your naming conventions so that no one is guessing at meanings or making up their own. While seemingly simple, this is the kind of disorganization that often wreaks havoc on data analysis.
7. Beware of duplication!
This may seem like a no-brainer, but duplication is a rather common mistake made when preparing data for analysis. Duplicating even one tiny input will inaccurately skew your data, resulting in corrupt predictions or poor decision-making. Be certain to “dedupe” your data in the preparation stages to be sure to remove any traces of duplication that may unevenly affect your data set.
8. Analyzing dirty data.
Take out the broom and dust bin! You need clean data! And the preparation phase is the time to ensure that your data is sparkling clean.
Cleaning data can take time. Excel-type formulas and macros can help identify errors in data that might make it corrupt. We already discussed the necessity of avoiding duplicate data, but in addition to that, we suggest cleaning up by identifying outliers, incorrect data, missing data, or data that simply does not make logical sense. Know what you’re dealing with before you get started.
9. Misconnecting the data.
If you’re working with a large data set, you’ll likely have information pouring in from various sources. Even with smaller data sets, you’ll have to input or merge data from at least one or two sources. You want to be certain that your spreadsheet or dashboard is pulling data from the correct source or sources and that the data is compatible with your data collection software. Essentially you need to be sure that your computer is playing nicely with all of the data and with the computer or software that is housing the original data. Connectivity is key and we recommend working to keep these data connectivity relationships as simple as possible. Multiple data sources mean that there are many more chances for error. Know your sources and manage them up front when preparing data for analysis. This way you can aim to avoid any unnecessary surprises down the road.
10. Using outdated data.
Using outdated data could happen if there’s a problem with your data source integration or inputting (see #9). Pay particularly close attention to timelines if you’re charged with examining real-time data. But it is of course best to be cautious when studying data through any specific timeframe. Be certain that you’ve checked that the data is up to date across platforms and that you are consistently pulling data from the same timeframe.
An Easier Data Preparation Solution?
While there’s not much that’s especially easy about preparing data for analysis, there are tools to help safely and accurately automate much of the data preparation and integration process.
Luckily advanced all-in-one online business dashboards like Cyfe exist to eliminate most of the manual data preparation necessary for a fully-functional data review and report. These help to eliminate the chance for common errors and ultimately result in more accurate and better quality data. Proper data analysis preparation will always take some time and that time will vary depending on the size of the data set and the goal or goals of the data analysis. But having an eye toward which common data preparation mistakes to avoid combined with an expert dashboard that helps automate data collation is a recipe for far more certain success in data analysis.
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