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

Big Data Data Analytics Data Quality

Optimize big data solution: warehouses, lakes, lakehouses compared.

Which Big Data Solution Is Best for You? Comparing Warehouses, Lakes, and Lakehouses

Big data makes the world go round. Well, maybe that’s an exaggeration — but not by much. Targeted promotions, behavioral marketing, and back-office analytics are vital sectors fueling the digital economy. To state it plainly: companies that leverage informational intelligence significantly boost their sales.

But making the most of available data options requires tailoring a platform that serves your company’s goals, protocols, and budget. Currently, three digital storage options dominate the market: data warehouses, data lakes, and data lakehouses. How do you know which one is right for you? Let’s unpack the pros and cons of each.

Data Warehouse

Data warehouses feature a single repository from which all querying tasks are completed. Most warehouses store both current and historical data, allowing for a greater breadth of reporting and analytics. Incoming items may originate from several sources, including transactional data, sales, and user-provided information, but everything lands in a central depot. Data warehouses typically use relational tables to build profiles and analysis metrics.

Note, however, that data warehouses only accommodate structured data. That doesn’t mean unstructured data is useless in a warehouse environment. But incorporating it requires a cleaning and conversion process.

Pros and Cons of Data Warehouses


  • Data Standardization: Since data warehouses feature a single repository, they allow for a high level of company-wide data standardization. This translates into increased accuracy and integrity.
  • Decision-Making Advantages: Because of the framework’s superior reporting and analytics capabilities, data warehouses naturally support better decision-making.


  • Cost: Data warehouses are powerful tools, but in-house systems are costly. According to Cooldata, a one-terabyte warehouse that handles about 100,000 queries per month can run a company nearly $500,000 for the initial implementation, in addition to a sizable annual sum for necessary updates. However, new AI-driven platforms allow companies of any size to design and develop their data warehouse in a matter of days, plus at a fraction of the price. 
  • Data Type Rigidity: Data warehouses are great for structured data but less so for unstructured items, like log analytics, streaming, and social media bits. Resultantly, it’s not ideal for companies with machine learning goals and aspirations.

Data Lake

Data lakes are flexible storage repositories that can handle structured and unstructured data in raw formats. Most systems use the ELT method: extract, load, and then transform. So, unlike data warehouses, you don’t need to clean informational items before routing them to data lakes because the schema is undefined upon capture.

At first, data lakes may sound like the perfect solution. However, they’re not always a wise choice — data lakes get very messy, very quickly. Ensuring the integrity and effectiveness of in-house systems takes several full-time workers who do nothing else but babysit the integrity of the lake.

Pros and Cons of Data Lakes


  • Ease and Cost of Implementation: Data lakes are much easier to set up than data warehouses. As such, they’re also considerably less expensive.
  • Flexibility: Data lakes allow for more data-type and -form flexibility. Moreover, they’re equipped to handle machine learning and predictive analytics tasks.


  • Organizational Hurdles: Keeping a data lake organized is like trying to keep a kid calm on Christmas morning: near impossible! If your business model requires precision data readings, data lakes probably aren’t the best option.
  • Hidden Costs: Staffing an in-house data lake pipeline can get costly fast. Data lakes can be exceptionally useful, but they require strict supervision. Without it, lakes devolve into junkyards.
  • Data Redundancy: Data lakes are prone to duplicate entries because of their decentralized nature.

Data Lakehouse

As you may have already guessed from the portmanteau, data lakehouses combine the features of data warehouses and lakes. Like the former, lakehouses operate from a single repository. Like the latter, they can handle structured, semi-structured, and unstructured data, allowing for predictive analytics and machine learning.

Pros and Cons of Data Lakehouses


  • Cost-Effective: Since data lakehouses use low-cost, object-storage methods, they’re typically less expensive than data warehouses. Additionally, since they operate off a single repository, it takes less manpower to keep lakehouses organized and functional.
  • Workload Variety: Since lakehouses use open-data formats and come with machine learning libraries like Python/R, it’s easier for data engineers to access and utilize the data.
  • Improved Security: Compared to data lakes, data lakehouses are much easier to keep secure.


  • Potential Vulnerabilities: As with all new technologies, hiccups sometimes arise after implementing a data lakehouse. Plus, bugs may still lurk in the code’s dark corners. Therefore, budgeting for mishaps is wise.
  • Potential Personnel Problems: Since data lakehouses are the new kid on the big data block, it may be more difficult to find in-house employees with the knowledge and know-how to keep the pipeline performing.

Big data collection, storage, and reporting options abound. The key is finding the right one for your business model and needs.

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.

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 Quality

How to Master Modern Data Governance

Data governance, though often overlooked, offers businesses a host of benefits. Keeping data up-to-date, accurate and complete often poses challenges for many business leaders. Thankfully, with the proper knowledge, tools, and patience, data and analytics leaders can build a team and utilize various available support systems to overcome these barriers and master data governance within their organization. 

What Is Data Governance and Why Is It Important?

At its core, data governance focuses on the following: 

  • Keeping data accurate and updated as needed
  • Controlling how, where, when, and by whom data is used within a company
  • Managing data integrity
  • Detecting, deleting, and merging duplicate data files within the file system
  • Ensuring all data reports are correct for compliance and regulatory purposes

Therefore, it’s obvious why data governance is an essential part of most workplace operations. Many businesses heavily rely on storing and retrieving information for future use. For this reason, duplicate information, customer profiles, and disorganized data tracking can lead to significant issues. Without correctly managed data, numerous departments can struggle to perform their jobs correctly. These issues can result in a loss of productivity, increased costs, and even impact long-term customer retention. 

Finally, it’s also important to note that storing data correctly and carefully monitoring how, when, where, and who uses stored data is also essential. Several regulatory agencies require companies to report on how they store and use consumer data. Others monitor data use and enforce transparency regarding certain types of information. Though, monitoring and governing data then become fundamentals to remaining in compliance with these regulatory agencies. 

How Can Companies Master Data Governance?

Mastering data governance is no easy task, but it is critical to most businesses, no matter the size. Thankfully, through the help of available tools and the assistance of data and analytics professionals, data governance becomes a manageable task. Here are a few key strategies organizations use to organize, analyze and maintain data integrity successfully. 

Determine the needs of the organization and align them with data governance solutions. This step serves as the stepping stone for all data governance plans. Many companies find themselves frustrated with the way data is managed across the departments, as governance practices are often mistakenly data-based rather than business-based. Determining how employees use data, how often it is retrieved and accessed, and who can make permanent changes to records allows organizations to manage their information effectively. 

Determine key performance indicators. During this phase, data and analytics leaders should also consider outlining and implementing key performance indicators, or KPIs, for managing their data. KPIs allow businesses to use measurable metrics to determine the overall success of their data governance practices. Over time, organizations can use these KPIs to make adjustments to their data governance plans. By measuring KPIs, data governance becomes a practice of using data to align with business needs and moves away from the traditional expectations of data storage. 

Develop risk management and security measures for stored data. Finally, many governing agencies require companies who store data to remain accountable and transparent regarding data security. Therefore, modern data governance plans include a variety of layers of protection. Companies should consider the following when developing their risk management programs: 

  • Who is interacting with private information regularly, and have they received the required compliance training?
  • When do individuals need to access stored data, and when can they change it?
  • What measures are in place to protect outsiders from accessing private information?

This step often involves working alongside your cyber security and legal teams to determine the appropriate action steps for data security. 

Who Should Understand Data Governance?

Ultimately, any individual within an organization who may access, store or update data used by a company should receive training on data governance. Once you’ve developed a high-quality governance plan, ensuring each individual within your company who interacts with stored data understands the organization’s data governance practices is essential. 

Furthermore, ensuring data integrity and accuracy may involve revisiting certain practices, changing methodologies, updating information, and providing additional company-wide training. Therefore, mastering modern data governance requires organization-wide cooperation and consistent monitoring to keep data consistent and error-free.

Big Data Data Analytics Data Enrichment Data Quality

5 Common Challenges of Data Integration (And How to Overcome Them)

Big data is a giant industry that generates billions in annual profits. By extension, data integration is an essential process in which every company should invest. Businesses that leverage available data enjoy exponential gains.

What is Data Integration?

Data integration is the process of gathering and merging information from various sources into one system. The goal is to direct all information into a central location, which requires:

  • On-boarding the data
  • Cleansing the information
  • ETL mapping
  • Transforming and depositing individual data pieces

Five Common Data Integration Problems

Getting a data integration process purring like a finely tuned Ferrari takes expertise, and the people running your system should intimately understand the five most common problems in an informational pipeline.

#1: Variable Data From Disparate Sources

Every nanosecond, countless bytes of data are moving rapidly around the ether — and uniformity isn’t a requirement. As a result, the informational gateway of any database or warehouse is a bit chaotic. Before data can be released into the system, it needs to be checked in, cleaned, and properly dressed.

#2: The Data/Security Conundrum

One of the most challenging aspects of maintaining a high-functioning data pipeline is determining the perfect balance between access and security. Making all files available to everyone isn’t wise. However, the people who need it should have it. When departments are siloed and have access to different data, inefficiencies frequently arise. 

#3: Low-Quality Information

A database is only as good as its data. If junk goes in, then waste comes out. Preventing your system from turning into an informational landfill requires scrubbing your data sets of dreck.

#4: Bad Integration Software

Even if your data shines like the top of the Chrysler Building, clunky data integration software can cause significant issues. For example, are you deploying trigger-based solutions that don’t account for helpful historical data?

#5: Too Much Useless Data

When collected thoughtfully and integrated seamlessly, data is incredibly valuable. But data hoarding is a resource succubus. Think about the homes of hoarders. Often, there’s so much garbage lying around that it’s impossible to find the “good” stuff. The same logic applies to databases and warehouses.

What Are Standard Data Integration Best Practices?

Ensuring a business doesn’t fall victim to the five pitfalls of data integration requires strict protocols and constant maintenance. Standard best practices include:

  • Surveillance: Before accepting a new data source, due diligence is key! Vet third-party vendors to ensure their data is legitimate.
  • Cleaning: When information first hits the pipeline, it should be scrubbed of duplicates and scanned for invalid data.
  • Document and Distribute: Invest in database documentation! Too many companies skip this step, and their informational pipelines crumble within months.
  • Back it Up: The world is a chaotic place. Anomalies happen all the time — as do mistakes. So back up data in the event of mishaps.
  • Get Help: Enlist the help of data integration experts to ensure proper software setups and protocol standards.

Data Integration Expertise and Assistance

Is your business leveraging its data? Is your informational pipeline making money or wasting it? If you can’t answer these questions confidently and want to explore options, reach out to Inzata Analytics. Our team of data integration experts can do a 360-degree interrogation of your current setup, identify weak links, and outline solutions that will allow you to move forward more productively and profitably.

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.

Big Data Data Analytics Data Quality

What is Data Integrity & Why is it Important in Data Analytics

What is Data Integrity?

Data integrity is the measure of accuracy, consistency, and completeness of an organization’s data. This also includes the level of trust the organization places on its data’s validity and veracity throughout its entire life cycle.

As a core component of data management and data security, data integrity revolves around who has access to the data, who is able to make changes, how it’s collected, inputted, transferred, and ultimately how it’s maintained over the course of its life.

Companies are subject to guidelines and regulations from governing organizations such as the GDPR to maintain certain data integrity best practices. Requirements are particularly critical for companies in the healthcare and pharmaceutical industry but remain important to decision-making across all sectors. 

Why is Data Integrity Important?

Data integrity is important for a number of reasons, key factors include:

  • Data Reliability & Accuracy – Reliable and accurate data is key to driving effective decision-making. This also assists employees in establishing trust and confidence in their data when making pivotal business decisions.
  • Improving Reusability – Data integrity is important to ensure the current and future use of an organization’s data. Data can be more easily tracked, discovered, and reused when strong integrity is maintained.
  • Minimizing Risks – Maintaining a high level of integrity can also minimize the dangers and common risks associated with compromised data. This includes things such as the loss or alteration of sensitive data.

Risks of Data Integrity

If data integrity is important to mitigating risks, what risks are involved? 

Many companies struggle with challenges that can weaken one’s data integrity and cause additional inefficiencies. Some of the most common risks to be aware of are the following:

  • Human Error – Mistakes are bound to happen, whether they be intentional or unintentional. These errors can occur when proper standards are not followed, if the information is recorded or inputted incorrectly, or in the process of transferring data between systems. While this list is not exhaustive, all of these are able to put the integrity of an organization’s data at risk.
  • Transfer Errors – Transferring data from one location to another is no small task, leaving room for possible errors during the transfer process. This process can result in altering the data and other table inaccuracies.
  • Hardware Problems – Though technology has come a long way by the means of hardware, compromised hardware still poses a risk to data integrity. Compromised hardware can cause problems such as limited access to data or loss of the data entirely.

Data Integrity vs. Data Quality

Are data integrity and data quality the same thing? No, despite their similar definitions and joint focus on data accuracy and consistency, data integrity and data quality are not one and the same.

Data quality is merely one component of data integrity as a whole. Integrity stems beyond whether the data is both accurate and reliable and instead also governs how data is recorded, stored, transferred, and so on. This extension of components, particularly when it comes to the additional context surrounding the data’s lifespan, is where the primary distinction between the two lies.

To sum up, data integrity plays a deciding role in ensuring accurate data that can be easily discovered, maintained, and traced back to its original data source.

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Why We Build Data Warehouses

What is a Data Warehouse?

A data warehouse is where an organization stores all of its data collected from disparate sources and various business systems in one centralized source. This aggregation of data allows for easy analysis and reporting with the ultimate end goal of making informed business decisions.

While data from multiple sources is stored within the warehouse, data warehouses remain separate from operational and transactional systems. Data flows from these systems and is cleansed through the ETL process before entering the warehouse. This ensures the data, regardless of its source, is in the same format which improves the overall quality of data used for analysis as a result.

There are many additional advantages to implementing a data warehouse. Some key benefits of data warehouses include the following:

  • Enhanced business intelligence and reporting capabilities
  • Improved standardization and consistency of data
  • Centralized storage increases accessibility to data
  • Better performance across systems
  • Reduced cost of data management

Why is a Data Warehouse Important?

Data warehouses are important in that they increase flexible access to data as well as provide a centralized location for data from disparate sources.

With the rapidly increasing amounts of operational data being created each day, finding the data you need is half the battle. You’re likely using multiple applications and collecting data from a number of sources. Each of these sources recording data in its own unique format.

Say you want to figure out why you sold a higher volume of goods in one region compared to another last quarter. Traditionally, you would need to find data from your sales, marketing, and ERP systems. But how can you be certain this information is up to date? Do you have access to each of these individual sources? How can you bring this data together in order to even begin analyzing it?

These questions depict how a simple query can quickly become an increasingly time consuming and complex process without the proper infrastructure. Data warehouses allow you to review and analyze all of this data in one unified place, developing a single source of data truth in your organization. A single query engine is able to present data from multiple sources, making accessibility to data from disparate sources increasingly flexible.

Why We Build Data Warehouses

At the end of the day, data warehouses help companies answer questions. What types of employees are hitting their sales targets? Which customer demographics are most likely to cancel their subscription? Why are we selling more through partnerships and affiliates compared to email marketing? 

Questions like these arise by the handful throughout the course of everyday business. Companies need to be able to answer these questions fast in order to quickly respond to change. Data warehouses empower businesses with the answers they need, when they need them.

Big Data Data Analytics Data Quality

The Fundamentals of Mastering Metadata Management

Poor data quality is estimated to cost organizations an average of $12.8 million per year. All methods of data governance are vital to combating this rising expense. While metadata has always been recognized as a critical aspect of an organization’s data governance strategy, it’s never attracted as much attention as flashy buzzwords such as artificial intelligence or augmented analytics. Metadata has previously been viewed as boring but inarguably essential. With the increasing complexity of data volumes, though, metadata management is now on the rise. 

According to Gartner’s recent predictions for 2024, organizations that use active metadata to enrich their data will reduce time to integrated data by 50% and increase the productivity of their data teams by 20%. Let’s take a deeper look into the importance of metadata management and its critical factors for an organization.

What is Metadata?

Metadata is data that summarizes information about other data. In even shorter terms, metadata is data about other data. While this might sound like some form of data inception, metadata is vital to an organization’s understanding of the data itself and the ease of search when looking for specific information. 

Think of metadata as the answer to the who, what, when, where, and why behind an organization’s data. When was this data created? Where did this data come from? Who is using this data? Why are we continuing to store this information?

There are many types of metadata, these are helpful when it comes to searching for information through various key identifiers. The two primary forms of metadata include:

  • Structural – This form of metadata refers to how the information is structured and organized. Structural metadata is key to determining the relationship between components and how they are stored.
  • Descriptive – This is the type of data that presents detailed information on the contents of data. If you were looking for a particular book or research paper, for example, this would be information details such as the title, author name, and published date. Descriptive metadata is the data that’s used to search and locate desired resources.
  • Administrative – Administrative metadata’s purpose is to help determine how the data should be managed. This metadata details the technical aspects that assist in managing the data. This form of data will indicate things such as file type, how it was created, and who has access to it. 

What is Metadata Management?

Metadata management is how metadata and its various forms are managed through processes, administrative rules, and systems to improve the efficiency and accessibility of information. This form of management is what allows data to easily be tracked and defined across organizations.

Why is Metadata Management Important?

Data is becoming increasingly complex with the continually rising volumes of information today. This complexity highlights the need for robust data governance practices in order to maximize the value of data assets and minimize risks associated with organizational efficiency.

Metadata management is significant to any data governance strategy for a number of reasons, key benefits of implementing metadata processes include:

  • Lowered costs associated with managing data
  • Increases ease of access and discovery of specific data
  • Better understanding of data lineage and data heritage
  • Faster data integration and IT productivity

Where is this data coming from?

Show me the data! Not only does metadata management assist with data discovery, but it also helps companies determine the source of their data and where it ultimately came from. Metadata also makes tracking of alterations and changes to data easier to see. Altering sourcing strategies or individual tables can have significant impacts on reports created downstream. When using data to drive a major company decision or a new strategy, executives are inevitably going to ask where the numbers are coming from. Metadata management is what directs the breadcrumb trail back to the source. 

With hundreds of reports and data volumes constantly increasing, it can be extremely difficult to locate this type of information amongst what seems to be an organizational sea of data. Without the proper tools and management practices in place, answering these types of questions can seem like searching for the data needle in a haystack. This illuminates the importance of metadata management in an organization’s data governance strategy.

Metadata Management vs. Master Data Management

This practice of managing data is not to be confused with Master Data Management. The two have similar end goals in mind when it comes to improving the capability and administration of digital assets. But managing data is not all one and the same, the practices are different through their approaches and structural goals. Master data management is more technically weighted to streamline the integration of data systems while metadata management focuses on simplifying the use and access of data across systems.


Metadata management is by no means new to the data landscape. Each organization’s use case of metadata will vary and evolve over time but the point of proper management remains the same. With greater data volumes being collected by companies than ever before, metadata is becoming more and more critical to managing data in an organized and structured way, hence its rising importance to one’s data management strategy.

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