Data Preparation Data Quality

Top 3 Risks of Working with Data in Spreadsheets

Microsoft Excel and Google Sheets are the first choices of many users when it comes to working with data. They’re readily available, easy to learn, and support universal file formats. When it comes to using a spreadsheet application like Excel or Google Sheets, the point is to present data in a neat, organized manner that is easy to comprehend. They’re also on nearly everyone’s desktop and were probably the first data-centric software tool any of us learned.

While spreadsheets are popular, they’re far from the perfect tool for working with data. There are some important risks to be aware of. We’re going to explore the top three things you need to be aware of when working with data in spreadsheets.

Risk #1: Beware of performance and data size limits in spreadsheet tools 

Most people don’t check the performance limits in spreadsheet tools before they start working with them. That’s because the majority won’t run up against them. However, if you start to experience slow performance, it might be a good idea to refer to the limits below to measure where you are and make sure you don’t start stepping beyond them.

Like I said above, spreadsheet tools are fine for most small data, which will suit the majority of users. But at some point, if you keep working with larger and larger data, you’re going to run into some ugly performance limits. When it happens, it happens without warning and you hit the wall hard.

Excel Limits

Excel is limited to 1,048,576 rows by 16,384 columns in a single worksheet.

  • A 32-bit Excel environment is subject to 2 gigabytes (GB) of virtual address space, shared by Excel, the workbook, and add-ins that run in the same process.
  • 64-bit Excel is not subject to these limits and can consume as much memory as you can give it. A data model’s share of the address space might run up to 500 – 700 megabytes (MB) but could be less if other data models and add-ins are loaded.

Google Sheets Limits

  • Google Spreadsheets are limited to 5,000,000 cells, with a maximum of 256 columns per sheet. (Which means the rows limit can be as low as 19,231 if your file has a lot of columns!)
  • Uploaded files that are converted to the Google spreadsheets format can’t be larger than 20 MB and need to be under 400,000 cells and 256 columns per sheet.

In real-world experience, running on midrange hardware, Excel can begin to slow to an unusable state on data files as small as 50MB-100MB. Even if you have the patience to operate in this slow state, remember you are running at redline. Crashes and data loss are much more likely!

(If you’re among the millions of people who have experienced any of these, or believe you will be working with larger data, why not check out a tool like Inzata, designed to handle profiling and cleaning of larger datasets?)

Risk #2: There’s a real chance you could lose all your work just from one mistake

Spreadsheet tools lack any auditing, change control, and meta-data features that would be available in a more sophisticated data cleaning tool. These features are designed to act as backstops for any unintended user error. Caution must be exercised when using them as multiple hours of work can be erased in a microsecond.

Accidental sorting and paste errors can also tarnish your hard work. Sort errors are incredibly difficult to spot. If you forget to include a critical column in the sort, you’ve just corrupted your entire dataset. If you’re lucky enough to catch it, you can undo it, if not, that dataset is now ruined, along with all of the work you just did. If the data saves to disk while in this state, it can be very hard, if not impossible, to undo the damage.

Risk #3: Spreadsheets aren’t really saving you any time

Spreadsheets are fine if you just have to clean or prep data once, but that is rarely the case. Data is always refreshing, new data is continually coming online. Spreadsheets lack any kind of repeatable processes and or intelligent automation.

If you spend 8 hours cleaning a data file one month, you’ll have to repeat nearly all of those steps the next time a refreshed data file comes along. 

Spreadsheets can be pretty dumb sometimes. They lack the ability to learn. They rely 100% on human intelligence to tell them what to do, making them very labor-intensive.

More purpose-designed tools like Inzata Analytics allow you to record and script your cleaning activities via automation. AI and Machine Learning let these tools learn about your data over time. If your Data is also staged throughout the cleaning process, and rollbacks are instantaneous. You can set up data flows that automatically perform cleaning steps on new, incoming data. Ultimately, this lets you get out of the data cleaning business almost permanently.

To learn more about cleaning data, download our guide: The Ultimate Guide to Cleaning Data in Excel and Google Sheets

Data Preparation Data Quality

Cleaning Your Dirty Data: Top 6 Strategies

Cleaning data is essential to making sure that data science projects are executed with the highest level of accuracy possible. Manual cleaning calls for extensive work, though, and it also can induce human errors along the way. For this reason, automated solutions, often based on basic statistical models, are used to eliminate flawed entries. It’s a good idea, though, to develop some understanding of the top strategies for dealing with the job.

Pattern Matching

A lot of undesirable data can be cleaned up using common pattern-matching techniques. The standard tool for the job is usually a programming language that handles regular expressions well. Done right, a single line of code should serve the purpose well.

1) Cleaning out and fixing characters is almost always the first step in data cleaning. This usually entails removing unnecessary spaces, HTML entity characters and other elements that might interfere with machine or human reading. Many languages and spreadsheet applications have TRIM functions that can rapidly eliminate bad spaces, and regular expressions and built-in functions usually will do the rest.

2) Duplicate removal is a little trickier because it’s critical to make sure you’re only removing true duplicates. Using other good data management techniques will make duplicate removal simpler, such as indexing. Near-duplicates, though, can be tricky, especially if the original data entry was performed sloppily.

Efficiency Improvement

While we tend to think of data cleaning as mostly preparing information for use, it also is helpful in improving efficiency. Storage and processing efficiency are both ripe areas for improvement.

3) Converting fields makes a big difference sometimes to storage. If you’ve imported numerical fields, for example, and they all appear in text columns, you’ll likely benefit from turning those columns into integers, decimals or floats.

4) Reducing processing overhead is also a good choice. A project may only require a certain level of decimal precision, and rounding off numbers and storing them in smaller memory spaces can speed things up significantly. Just make sure you’re not kneecapping required decimal precision when you use this approach.

Statistical Solutions

Folks in the stats world have been trying to find ways to improve data quality for decades. Many of their techniques are ideal for data cleaning, too.

5) Outlier removal and the use of limits are common ways to analyze a dataset and determine what doesn’t belong. By analyzing a dataset for extreme and rare data points, you can quickly pick out what might be questionable data. Be careful, though, to recheck your data afterward to verify that low-quality data was removed rather than data about exceptional outcomes.

Limiting factors also make for excellent filters. If you know it’s impossible for an entry to register a zero, for example, installing a limit above that mark can eliminate times when a data source simply returned a blank.

6) Validation models are useful for verifying that your data hasn’t been messed up by all the manipulation. If you see validation numbers that scream that something has gone wrong, you can go back through your data cleaning process to identify what might have misfired.

Big Data Data Preparation

6 Core Principles Behind Data Wrangling

What Is Data Wrangling? 

What is data wrangling? Also known as data cleaning, data remediation, and data munging, data wrangling is the digital art of molding and classifying raw information objects into usable formats. Practitioners use various tools and methods — both manual and automated — but approaches vary from project to project depending on the setup, goal, and parameters.

Why Is Data Wrangling Important?

It may sound cliche, but it’s true: data is the gold of today’s digital economy. The more demographic information a company can compile about extant customers and potential buyers, the better it can craft its marketing campaigns and product offerings. In the end, quality data will boost the company’s bottom line.

However, not all data is created equal. Moreover, by definition, informational products can only be as good as the data upon which they were built. In other words, if bad data goes in, then bad data comes out.

What Are the Goals of Data Wrangling?

Data wrangling done right produces timely, detailed information wrapped in an accessible format. Typically, businesses and organizations use wrangled data to glean invaluable insights and craft decision frameworks.

What Are the Six Core Steps of Data Wrangling?

The data remediation scaffolding consists of six pillars: discovery, structuring, cleaning, enriching, validating, and publishing.


Before implementing improvements, the current system must be dissected and studied. This stage is called the discovery period, and it can take anywhere from a few days to a few months. During the discovery phase, engineers unearth patterns and wrap their heads around the best way to set up the system.


After you know what you are working with, the structuring phase begins. During this time, data specialists create systems and protocols to mold the raw data into usable formats. They also code paths to distribute the information uniformly. 


Analyzing incomplete and inaccurate data can do more harm than good. So next up is cleaning. This step mainly involves scrubbing incoming information of null values and extinguishing redundancies. 


Companies may use the same data, but what they do with it differs significantly. During the enriching step of a data wrangling process, proprietary information is added to objects, making them more useful. For example, department codes and meta information informed by market research initiatives may be amended to each object.


Testing — or validating — is the backbone of all well-executed data systems. During this phase, engineers double-check to ensure the structuring, cleaning, and enriching stages were processed as expected. Security issues are also addressed during validation.


The end product of data wrangling is publication. If the information is headed to internal departments or data clients, it’s typically deployed through databases and reporting mechanisms. If the data is meant for promotional materials, then copywriting, marketing, and public relations professionals will likely massage the information into relatable content that tells a compelling story. 

Data Wrangling Examples

We’ve discussed the ins and outs of data wrangling procedures; now, let’s review common examples. Data wranglers typically spend their days:

  • Finding data gaps and deciding how to handle them
  • Analyzing notable outliers in the data and deciding what to do about them
  • Merging raw data into a single database or data warehouse
  • Scrubbing irrelevant and unnecessary information from raw data

Are you in need of a skilled data wrangler? The development of AI-powered platforms, such as Inzata Analytics, has rapidly expedited the process of cleaning and wrangling data. As a result, professionals save hours on necessary tasks that can transform your data landscape and jump-start profits.

Data Preparation Data Quality

Content Tagging: How to Deal With Video and Unstructured Data

Working with unstructured video data can be extremely difficult to tame! But don’t worry. With a few handy tips, the process becomes a lot more manageable.

Please note: this is our second article in a series on unstructured data. Click here to read the first installment, which explores indexing and metadata.

What Is the Problem With Unstructured Data?

Unstructured information is an unwieldy hodgepodge of graphic, audio, video, sensory, and text data. To squeeze value from the mess, you must inspect, scrub, and sort the file objects before feeding them to databases and warehouses. After all, raw data is of little use if it cannot be adequately leveraged and analyzed.

What Is Content Tagging?

In the realm of information management, content tagging refers to the taxonomic structure established by an organization or group to label and sort raw data. You can think of it as added metadata.

Content tagging is largely a manual process. In a typical environment, people examine the individual raw files and prep them for data entry. Common tasks include:

  • Naming each item
  • Adding meta descriptions of images and videos
  • Splicing videos into frames
  • Separating and marking different mediums

How to Use Content Tagging to Sort Unstructured Data

You can approach content tagging in several ways. Though much of the work is best done manually, there are also ways to automate some processes. For example, if an incoming file ends with a .mov or .mp4 suffix, you can write a script that automatically tags it as a video. The same can be done for graphics and text documents.

Tagging helps organize unstructured data as it provides readable context atop which queries can be crafted. It also allows for pattern establishment and recognition. In fact, photo recognition programs are, in large part, fueled by extensive tagging.

The Pros and Cons of Content Tagging

Tagging has its pros and cons. The downside is the manual labor involved. Depending on the amount of inbound data, it could take considerable resources to get the job done. Many businesses prefer to enlist third-party database management teams to mitigate costs and free up personnel.

As for pros, there are a couple. Firstly, content tagging makes data organization much more manageable. When you label, sorting becomes a snap. Secondly, tagging adds more value to data objects, which allows for better analysis.

Let’s Transform Your Unstructured Data

Leveraging AI-powered tools to perform complex data management tasks can save you money and increase efficiency in the long run. Inzata Analytics maintains a team of experts that focuses on digital data, analytics, and reporting. We help businesses, non-profits, and governments leverage information technology to increase efficiency and profits.

Get in touch. Let’s talk. We can walk you through the advantages of AI-powered data management and how it can boost your bottom line. See a demo of the platform here.

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Big Data Data Preparation

Indexing & Metadata: How to Deal with Video and Unstructured Data

Solutions for Unstructured Data That Includes Video

If you’ve landed on this page, there’s a good chance you’re sitting on a mountain of unstructured data, specifically an abundance of video files. Your goal is to parse, organize, and distribute the information in such a way that makes it the most useful to the greatest number of people in your organization. But unstructured data can be as unruly and difficult to manage as a bag of snakes. So the question becomes: How can you tame it?

What’s the Problem With Unstructured Video Data?

So what’s the problem with unstructured data? As is the case with a tangle of wires, the hurdle with unstructured data is that it’s difficult to classify, manage, organize, and distribute. And ultimately, what’s the use of collecting loads of information if you can’t do anything with it? When videos are tossed into the mix, things become even more complicated because they’re not easily searchable in text-based database systems. 

But before you can develop a plan to sort out the mess, you must define the data goals. Ask yourself a few key questions such as:

  • Who needs access to the information? 
  • For what are they using it? 
  • How does the intended data use support the company’s overarching goals? 

Unstructured Video Data: Indexing

Indexing is a database optimization technique, which preprocesses information and allows for faster querying. It’s an advanced database administration skill that requires the programmer to account for many options, like missing values and form errors. 

When videos are in the data mix, indexing is even more complicated. However, by setting up a simple save-and-catalog function, it’s manageable. So how do you do it?

First, save the video file on the network. Make sure it’s somewhere accessible to the people who will need it. Also, ensure that people can’t change file names easily. If they do, it can “break” the database. Then, catalog each A/V file by including GUID keys that point to where they sit on the network. 

If greater specificity is needed, make a record — and corresponding line item — for each video frame. Yes, it’s time and labor-intensive, but the effort is often worth it to mine intelligent data.

Unstructured Video Data: Metadata

After creating the index, the next step is gathering, storing, and linking the appropriate metadata, which may include the date, length format, EXIF info, and source. Cataloging the metadata is vital because it provides a searchable and filterable field for the video file line item.

Sometimes, you may want to write some metadata to the file name as a backup. You can achieve this by structuring the file names like [DATE]_[GUID].mp4. By doing so, team members can quickly determine to which record the line item is tied.

Let’s Discuss Your Unstructured Data Needs

Outsourcing database logistics to a third party can be the ideal solution because it frees up internal resources for profit-generating activities. Plus, partnering with database experts can decrease costs associated with employment. 

Inzata Analytics’s team has considerable experience empowering businesses, non-profits, schools, and government entities to maintain their unstructured databases. Reach out today. Let’s start the conversation.

Big Data Data Preparation Data Quality

The Costly Compound Effect of Bad Data in Your Warehouse

Bad data can be seen as kryptonite to a company’s bottom line. Like a super spreader, it sneaks in, replicates, and corrodes your informational warehouse like waves on clay. And when that happens, trust is compromised, which can lead to additional risks and possible mishaps. After all, a company’s reputation and insight accuracy deeply impact its bottom line.

What is a Data Warehouse?

Data warehousing technology allows businesses to aggregate data and store loads of information about sales, customers, and internal operations. Typically, data warehouses are significantly larger than databases, hold historical data, and cull information from multiple sources.

If you’re interested in learning more about data warehouses, try reading: Why We Build Data Warehouses

Why is Data Warehousing Important to Your Bottom Line?

In today’s highly personalized digital marketing environment, data warehousing is a priority for many corporations and organizations. Although data warehouses don’t produce direct profits, the information and insights they facilitate act as beacons for corporate and industry trajectories. For some businesses, informational warehouses provide the data fuel needed to populate their apps and customer management systems.

What is Good Data?

A data warehouse is only as good as the information in it, which raises the question: what constitutes good data?

Informational integrity is tied to seven key pillars:

  1. Fitness: Is the data moving through the pipeline in a way that makes it accessible for its intended use?
  2. Lineage: From where is the info coming, and is it arriving at the proper locations?
  3. Governance: Who has access to the data throughout the pipeline? Who controls it?
  4. Stability: Is the data accurate?
  5. Freshness: Did it arrive on time?
  6. Completeness: Did everything that was supposed to arrive land?
  7. Accuracy: Is the information accurate?

Early Detection Saves Time and Money

The longer it takes to find a data pipeline issue, the more problems it creates — and the more it costs to fix. That’s why early detection is vital.

Data errors are like burrowing viruses. They sneak in and keep a low profile while multiplying and festering. Then one day, seemingly out of the blue, the error rears its ugly head and causes chaos. If you’re lucky, the problems stay internal. If you’re unlucky, the error has a catastrophic downstream effect that can erode confidence in your product or service. 

Examples: The Costly Compound Effect of Data Warehouse Errors

We’ve established that data warehouse errors are no-good, horrible, costly catastrophes. But why?

Upstream Data Provider Nightmare

Imagine if other companies rely on your data to fuel their apps, marketing campaigns, or logistics networks. A mistake that manifests from your camp could have a disastrous domino effect that leads to a client-shedding reputation crisis.

Late-Arriving Data

Late-arriving data is another nightmare if other companies rely on your data. Think of it as a flight schedule. If one plane arrives late, it backs up every other flight that day and may force cancellations to get the system back on track.

Understanding Leading Indicators of Data Warehousing Issues

Leading indicators signal that bad data has weaseled its way into a data pipeline. However, built-in status alerts may not always work. For example, it’s possible to receive a 200 success response from an API built on the HTTPS protocol since the check only applies to the connection, not the data transfer. Intrinsically, it’s essential to understand the leading error indicators.

Catch data pipeline leading error indicators by:

  • Setting up baselines
  • Establishing data checkpoints
  • Tracking data lineage
  • Taking metric measurements

Maintaining a healthy data warehouse is of vital importance, especially if other businesses rely on your services. Working with data warehousing solutions is often the best option in terms of cost optimization, speed, and overall performance. They have the skills, tools, and institutional knowledge to ensure everything runs smoothly.

Artificial Intelligence Data Analytics Data Preparation

Growth Hacking Your Business Processes with Artificial Intelligence

Along with data and analytics, the focus on continuous improvement remains a constant in the business world. Recent market disruptions have only emphasized the need for businesses to optimize their processes and core functions moving forward. Artificial intelligence is one tool companies are turning to when achieving greater efficiency within their operations. Let’s explore how AI is transforming the way we do business, from data cleaning to the customer experience.

Using AI to Cleanup Dirty Data

Let’s get straight to the point. Dirty data costs businesses money, regardless of if they heavily rely on or prioritize data in their operations. The average cost of dirty data sings to the tune of around 15% to 25% of revenue each year. While this percentage doesn’t appear to be an overwhelming amount, consider the overarching estimate from IBM that bad data costs the U.S. $3.1 trillion each year. This high cost is mainly due to the complexities associated with cleaning and maintaining an organization’s data quality. 

There’s no question that data cleaning is a lengthy and time-consuming process. As a result of this, less time is able to be devoted to high-level goals. Decision-makers have long wait times when it comes to converting raw data into actionable insights. AI, though, is able to automate this process so businesses can focus their efforts elsewhere. AI learns from each data set and can detect columns in need of cleaning, all while simultaneously updating the data model. The productivity of your data science team is improved, saving hundreds of hours that would have been spent on cleaning tasks.

Analyzing Business Data for Forecasting and Prediction

The use of business data to identify patterns and make predictions is well established. Using AI-powered tools and solutions, any business user can generate insights quickly without advanced programming or data science skills. The ease of use makes this process faster, more accessible, and efficient across business units. This reduces miscommunications between the analytics team and eliminates wait times on reports, query requests, and dashboard delivery.

Additionally, exploding data volumes have made effective use of an organization’s data difficult to manage. Artificial intelligence helps to quickly analyze these large volumes in record time, allowing for faster insights along with higher quality forecasting. Actionable business data is becoming accelerated with the use of AI, helping business leaders make decisions with greater accuracy.

Improving Sales and Customer Success

AI-powered analytics is helping companies gain insights into their prospects as well as current customers. For instance, companies can use AI in conjunction with their CRM data to predict which customers are most likely to cancel their subscriptions or which new sales accounts are more likely to close. These predictions can be flagged to alert the customer success team or sales staff, highlighting where they should be maximizing their time. This acceleration can also result in a more efficient and effective customer lifecycle.

On the customer experience side of things, process improvement can also be established through automatic support lines and AI-powered chatbots. AI systems can monitor customer support calls and detect detailed elements as minuscule as tone to continually keep an eye on quality. Chatbots also offer additional availability for immediate support. Problems are identified and resolved faster to increase revenue along with customer retention.

Big Data Data Preparation

Discrete Data vs. Continuous Data: What’s the Difference?

We create data every day, oftentimes without even realizing it. To put a number on it, it’s estimated that each day we create 2.5 quintillion bytes of data worldwide. Tasks as simple as sending a text message, submitting a job application, or streaming your favorite TV show are all included in this daily total. However, not all of this data is created equal.

Similar to the many unique ways there are to create data, there is also a corresponding array of various data types. Data types are important in determining how the data is ultimately measured and used to make assumptions.

Let’s get down to the fundamentals of numeric data types as we explore discrete data, continuous data, and their importance when it comes to Big Data and analytics.

Numeric Data Types

Numerical data types, or quantitative data, is what people typically think of when they hear the word “data.” Numerical data types express information in the form of numbers and assign numerical meaning to data. There are two primary types of numerical data: discrete and continuous data.

What is Discrete Data?

Discrete data also referred to as discrete values, is data that only takes certain values. Commonly in the form of whole numbers or integers, this is data that can be counted and has a finite number of values. These values must be able to fall within certain classifications and are unable to be broken down into smaller parts.

Some examples of discrete data would include:

  • The number of employees in your department
  • The number of new customers you signed on last quarter
  • The number of products currently held in inventory

All of these examples detail a distinct and separate value that can be counted and assigned a fixed numerical value. 

What is Continuous Data?

Continuous data refers to data that can be measured. This data has values that are not fixed and have an infinite number of possible values. These measurements can also be broken down into smaller individual parts.

Some examples of continuous data would include:

  • The height or weight of a person
  • The daily temperature in your city
  • The amount of time needed to complete a task or project

These examples portray data that can be placed on a continuum. The values can be continually measured at any point in time or placed within a range of values. The distinguishing factor being that the values are measured over time rather than fixed.

Continuous data is commonly displayed in visualizations such as histograms due to the element of variable change over time.

Discrete Data vs. Continuous Data

Discrete and continuous data are commonly confused with one another due to their similarities as numerical data types. The primary difference, though, between discrete and continuous data is that discrete data is a finite value that can be counted whereas continuous data has an infinite number of possible values that can be measured.

If you’re questioning whether or not you’re working with discrete or continuous data, try asking yourself questions such as:

  • Can these values be counted?
  • Can these values be measured?
  • Can these values be broken down into smaller parts and still make sense?

The Importance of Numerical Data Types

Discrete and continuous data both play a vital role in data exploration and analysis. Though it is easy to review definitions and straightforward examples, data is often filled with a mixture of data types. Making the need to be able to identify data types all the more important.

Additionally, many exploratory methods and analytical approaches only work with specific data types. For this reason, being able to determine the nature of your data will make handling your data more manageable and effective when it comes to yielding timely insights.

Big Data Data Analytics Data Preparation

The Beginner’s Guide to Data Streaming

What is Data Streaming?

Data streaming is when small bits of data are sent consistently, typically through multiple channels. Over time, the amount of data sent often amounts to terabytes and would be too overwhelming for manual evaluation. While everything can be digitally sent in real-time, it’s up to the software using it to filter what’s displayed. 

Data streaming is often utilized as an alternative to a periodic, batch data dump approach. Instead of grabbing data at set intervals, streamed data is received nearly as soon as it’s generated. Although the buzzword is often associated with watching videos online, that is only one of many possible implementations of the technology.

How is Data Streaming Used?

Keep in mind that any form of data may be streamed. This makes the possibilities involving data streaming effectively limitless. It’s proven to be a game-changer for Business Analytics systems and more. From agriculture to the fin-tech sector to gaming, it’s used all over the web.

One common industry application of data streaming is in the transportation and logistics field. Using this technology, managers can see live supply chain statistics. In combination with artificial intelligence, potential roadblocks can be detected after analysis of streamed data and alternative approaches can be taken so deadlines are always met.

Data streaming doesn’t only benefit employees working in the field. Using Business Analytics tools, administrators, and executives can easily see real-time data or analyze data from specific time periods. 

Why Should We Use Data Streaming?

Data silos and disparate data sources have plagued the industry for countless years. Data streaming allows real-time, relevant information to be displayed to those who need access to it the most. Rather than keeping an excessive amount of data tucked away on a server rarely accessed, this technology puts decision-driving information at the forefront.

Previously, this type of real-time view of business processes was seen as impossible. Now that it’s possible to have an internet connection almost everywhere, cloud computing makes live data streaming affordable, and Business Analytics tools are ready to implement data streaming, there’s no reason it would be inaccessible.  

While it may be tempting to stick to older ways of processing data, companies who don’t adapt to this new standard will likely find it more difficult to remain competitive over the years. Companies that do incorporate the technology will likely see their operations become more streamlined and find it much easier to analyze and adjust formerly inefficient processes.

Big Data Data Preparation Data Quality

How to Solve Your Data Quality Problem

Why Does My Data Quality Matter?

One of the prime goals of most data scientists is to maintain the quality of data in their domains. Because business analytics tools rely on past data to make present decisions, it’s critical that this data is accurate. While it’s plenty easy to continually log information, you can risk creating data silos, large quantities of data that end up never really being utilized. 

Your data quality can directly impact whether and to what degree your company succeeds. Bad data can never be completely filtered, even with the best BI tools. The only way to base a future business decision on quality data is to only collect quality data in the first place. If you’re noticing that your company’s data could use a quality upgrade, it’s not too late!

What Are Some Common Mistakes Leading to Bad Data Quality?

By simply not engaging in a few practices, your company can drastically cut back on the volume of bad data you store. First, remember that you shouldn’t automatically trust the quality of data being generated by your current enterprise tool suite. This should be evaluated by professional data scientists to determine quality. Quite often, older tools generate more junk data than modern tools with better filtering technology.

Another common mistake is to allow different departments within your company to isolate their data away from the rest of the company. Of course, depending on the department and nature of your company, this could be a legal requirement. However, if not, you should ensure that there’s a free flow of data across business units. This can create an informal “checks and balances” system and help prevent those data silos from building or destroy existing ones.

How Can I Identify Bad Data?

Keeping in mind that, even with the best practices in place, it’s unrealistic to expect a total elimination of risk associated with bad data being collected. With the volume of enterprise tools in usage combined with even the most minor human error in data entry having the potential to create bad data, a small amount should be expected. That’s why it’s important to remain vigilant and regularly check for these items in your existing data and purge those entries if found:

  • Factually False Information – One of the more obvious examples of bad data is data that’s entirely false. Almost nothing could be worse to feed into your BI tools, making this the first category of bad data to remove if found.
  • Incomplete Data Entries – Underscoring the importance of mandating important database columns, incomplete data entries are commonly found in bad data. These are entries that cannot be fully interpreted without the information that’s missing being filled in.
  • Inconsistently Formatted Information – Fortunately, through the power of regular expressions, this type of bad data can often be solved fairly quickly by data scientists. A very common form of this is databases of telephone numbers. For example, even if all of the users are in the same country, different formats like (555) – 555-5555, 5555555555, 555-5555555, etc., are often present when any string is accepted as a value for the column.

What Can I Do Today About Bad Data?

It’s crucial that your company comes up with a viable, long-term strategy to rid your company of bad data. Of course, this is typically an intensive task and isn’t accomplished overnight. Most importantly, the removal of bad data isn’t simply a one-time task. It must be something that your data staff is continuously evaluating in order to stay in place and remain effective.

After an initial assessment of your company’s data processing practices and the volume of bad data you have, a professional firm can consult with your data team for technical strategies they can utilize in the future. By combining programmatic data input and output techniques with employee and company buy-in, no bad data problem is too out of control to squash.

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Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter