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The value of data
The Economist declared data to be the world’s most valuable resource above oil or any other commodity. So if everything depends on the quality of your data, managing it properly should be essential to your workflows.
Data is an important asset regardless of business size or type. Small companies rely on a steady stream of data from customer signup forms to their CRM for promotion and billing. Large organizations need to unify data from various business units to make high-level decisions quickly.
Until recently, the main focus for a business was gathering data. But what happens when data is stuck in a data lake or stagnant system that doesn’t “talk” to others? A blockage in your data pipeline can cause major issues for all aspects of your business. Broken data flows feel overwhelming, and after a while, teams begin to lose hope in ever having a reliable stream of data.
We’ll take a look at the main symptoms of broken data flows to help you avoid that fate, and then diagnose how you can fix them before it’s too late.
How can data go wrong?
Data operations teams depend on functional, high-quality data processes to get what they need from their accumulation of information. But efficiently processing high-quality data is easier said than done. Moreover, data quality problems can cause extensive issues across several verticals. And if your data is bad, all the sophisticated machine learning tools in the world can’t save you. So, where exactly can data go wrong?
Inaccurate data leads you astray from a truthful picture of a situation. It might come in incorrect customer data, data resulting from human error, or analytics drawn from outdated information. Datastores might also experience downtimes during upgrades, migrations, and reorganizations. Data with an extra-long turnaround time is often rendered inaccurate when it reaches its destination, and data drawn during these downtimes may prove inaccurate.
Ambiguous data might be data with an untraceable source. If your system is processing large amounts of data at rapid speeds, column headings may move, formatting may change, and simple mistakes can snowball into flawed or unusable reporting. If data is ambiguous or untraceable, you’ll have problems drawing insights from it.
Similar to ambiguous data, unknown data is data that is rendered unusable for predictive analytics. It’s data that can’t actually be useful. If data resides in different CRM and storage systems, it might sit in a silo and stay untraced across the organization. If standalone data sites don’t speak to and inform the other sites in the system, that glitch will lead to unknown data. Only data that’s sorted, tagged, and processed through a functional data ops pipeline will be known.
Data typically fits into two categories: Structured and unstructured. Unstructured data like invoices, purchase orders, and financial statements are likely kept separate from other key information due to ease of processing. Unfortunately, these data types are often kept in different locations, making it harder to see a holistic picture of your data.
Overload of data
Bigger is not always better when it comes to your data. You need to quickly find the data that’s relevant to your business needs. When locating the correct data and utilizing it correctly is critical to business success, it’s possible to get lost in an overload of data.
Bad data problems
Understanding how data problems impact your business could help you avoid those problems in the first place. We’ve discussed how data can go wrong. Let’s take a look at the damaging effects of that bad data:
- Lost revenue — Bad data causes lost dollars and cents on both the product and internal efficiency levels.
- Missed opportunities — The impact of a broken data flow goes beyond current opportunities. It can derail you from potential new opportunities to do better work.
- Accidental fraud or noncompliance — Bad data governance can cause breaches in compliance and even land an organization in hot water.
- Business inefficiencies — A lack of transparency caused by bad data can make a business function in a roundabout fashion.
- Bad decision-making — Bad data can obscure the right path and cause businesses to make reckless decisions or strategic errors.
- Wasting time, money, and resources — Organizations spend so much time fixing broken data when their resources could otherwise be deployed to more profitable activities.
- Customer and employee frustration — Broken data often leads to frustrating customer experiences, which can negatively impact internal resources.
Fixing data quality issues
Building data quality checks into your workflows is a core task of any business. It’s not a one-and-done process, but instead, a practice that you’ll continually improve and tweak.
So how do you establish high-quality data flows, and avoid poor data quality issues altogether? Look to instill data discipline in the following ways:
- Accuracy — Is the data input correctly, using the correct numbers, names, and tags?
- Consistency — Does data go through the same process, even if it enters and exits at disparate points along the data lifecycle?
- Completeness — Does your data have gaps? Think about documents that come from customers or third party sources. These are often the sources of missing information.
- Timeliness — Is data entering the pipeline and flowing to other sources the moment it’s received?
- Singularness: Is there any overlap in your data? Understanding and reducing any redundancy means a lighter workload on systems that rely on data.
The data transformation
Quality data workflows aren’t just helpful — they’re essential for managing projects and delivering products and services. It can be tempting to rush to get projects going and put focusing on data workflows on the backburner. But doing so will almost definitely cause you bigger headaches down the line.
As technologies and techniques for aggregating and analyzing data continually grow more sophisticated, so do the demands on data for the modern organization. The ability to address data issues in the pipeline is a common point of failure, so there has to be a universal acknowledgment that data quality should be a focus to identify and alleviate these issues.
Prioritize data quality from the outset. If you’re aware of areas where data can go wrong, you can avoid data quality issues before they become catastrophes. Next, pay attention to your business strategy and align your data strategy to your overarching business goals. Finally, choose intelligent data technologies that allow you to unlock valuable data and pour it back into strategic analytics. Data should ultimately enrich your understanding of your customers, your product, and your successes and failures.