3 Data Problems Solved by Agile Data Mastering

When it comes to data quality and availability, there is a gap between expectations and reality.

The expectation is that reliable, integrated data is readily available in an easy-to-digest format that allows data consumers to harness key business insights.

The reality, however, is that data comes from a wide array of sources that aren’t easily accessible and are often times plagued with inaccuracies. Critical questions are either difficult or impossible to answer because of the high level of effort required to find, assemble, prep and analyze data.

This gap is due, in large part, to the approach that many organizations take to data mastering. Traditional processes and methods — such as Extract, Transform and Load (ETL) Master Data Management (MDM) — are slow. These methods can result in projects that take months or even years to complete. And they place too much burden on data scientists, who often have to spend up to 80% of their time cleaning and preparing data before they can begin their actual work.

This is where Agile Data Mastering (ADM) comes in. ADM is an approach that connects people, processes and tools to treat data unification as an iterative process. For enterprises, this means Agile Data Mastering solves some of the most common data problems they are currently facing, including:

  1. Data taking too long to prepare
  2. Not being able to analyze the data
  3. Not trusting the data

1. It Takes too Long to Prepare Data

In order to remain competitive and keep pace with ever-changing industry landscapes, businesses need to be able to make decisions in real-time. That’s why it’s crucial that the data they rely on to make these decisions is both accurate and up to date. The challenge is that ETL and MDM methods take time, because they center around rule creation that is extremely time consuming. Often times, teams are already dealing with out of date data by the time they are able to actually analyze it.

In contrast to this, Agile Data Mastering leverages machine learning to handle the heavy lifting of identifying relationships within data. ADM involves subject matter experts directly in the mastering process, but simplifies the approach by presenting them with suggestions about their data and leveraging their feedback to train sophisticated mastering models. No longer must technical staff struggle to correctly interpret the business domain knowledge of their users into a complex set of rules which must accommodate all of the idiosyncrasies of source data. For enterprises, the result is the ability to derive analytic insights in a matter or days or weeks.

2. Enterprises Can’t Analyze the Data

Often, data consumers are working with incomplete portions of their data — forcing stakeholders to make assumptions without knowing the whole story. This leads to flawed decisions, at best, and completely off base analysis, at worst.

This story of working with incomplete data is far too common across many organizations, due in large part to limitations of data mastering. In many cases, data is siloed across various systems and business units, and organizations lack the tools or experts to master it effectively. So they limit their work to the best known, most easily accessed data, which often do not comprise the necessary set of relevant sources.

Because the algorithmic approach can cover a broader set of data with minimal incremental effort, ADM makes it easy to quickly unify multiple datasets from a variety of sources, without compromising on accuracy. This means that enterprises are always working with all of the relevant data, in its most current form, and can react quickly to business changes knowing that they are leveraging a comprehensive view of their data.

3. The Data isn’t Trustworthy

Getting access to the data is one problem, but being able to glean reliable insights is another entirely. Many organizations know that their data is simply unreliable due to limited business expert involvement in data mastering and stewardship, but because they cannot wait for the data quality to be improved, they build analytical applications which themselves produce suboptimal results. A GIGO (Garbage In, Garbage Out) problem for sure. But because it traditionally takes so much time and effort to master data, end users can be faced with a difficult decision — do we want to be a little wrong, or do we want to be very late?

This is why it’s so important to embrace an agile mindset when it comes to data mastering. Agile approaches aren’t just about incremental value delivery, they’re also about incremental validation, and learning from experience. True data quality comes from moving beyond core rules to actually using the data and finding out where the data don’t align with reality. Agile data mastering involves experts directly, and provides them the opportunity to continually provide feedback which improves the quality of their data. Assisted by machine learning technology to more quickly unify and classify data at scale, enterprises can rapidly build a solid foundation and easily iterate upon it to achieve a holistic view of their data — and leverage that unified, clean data to drive analytic outcomes.

What This Means for Enterprises

Today’s data challenges require enterprises to change the way they evaluate their technology and processes. An agile approach to data mastering with support from machine learning completely transforms the process making it easy, efficient, and effective to accomplish.

To learn more about Agile Data Mastering, download our ebook below. And to find out how Tamr uses Agile Data Mastering to help enterprises unify data and solve data problems, please reach out or schedule a demo.

Agile Data Mastering

When it comes to data quality and availability, there is a gap between expectations and reality. Download this ebook to learn how Agile Data Mastering can solve common data challenges.

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Mark is the Head of Product at Tamr and is a 20-year veteran of Enterprise Data Management and Analytics software, Mark has held engineering, product management, and technology strategy roles at Lucent Technologies, Macrovision, and most recently at Lavastorm, where he was Chief Technology Officer.