Why Data Garbage-In means Analytics Garbage-Out..

This is the third in a series of blogs on Data Science that I am jointly authoring with Maleeha Qazi, (https://www.linkedin.com/in/maleehaqazi/). We have previously covered some of the inefficiencies that result from a siloed data science process @ http://www.vamsitalkstech.com/?p=5046 & the ideal way Data Scientists would like their models deployed for the maximal benefit and use – as a Service @ http://www.vamsitalkstech.com/?p=5321. As the name of this third blog post suggests, the success of a data science initiative depends on data. If the data going into the process is “bad” then the results cannot be relied upon. Our goal is to also suggest some practical steps that enterprises can take from a data quality & governance process standpoint. 

“However, under the strong influence of the current AI hype, people try to plug in data that’s dirty & full of gaps, that spans years while changing in format and meaning, that’s not understood yet, that’s structured in ways that don’t make sense, and expect those tools to magically handle it. ” – Monica Rogati (Data Science Advisor and ex-VP  Jawbone – 2017) [1]

Image Credit – The Daily Omnivore

Introduction

Different posts in this blog have discussed Data Science and other Analytical approaches to some degree of depth. What is apparent is that whatever the kind of analytics – descriptive, predictive, or prescriptive – the availability of a wide range of quality data sources is key. However, along with volume and variety of data, the veracity, or the truth, in the data is as important. This blog post discusses the main factors that determine the quality of data from a Data Scientist’s perspective.  

The Top Issues of Data Quality

As highlighted in the above illustration, the top quality issues that data assets typically face are the following:

1. Incomplete Data: The data provided for analysis should span the entire cross-section of known data about how the organization views its customers and products. This would include data generated from various applications that belong to the business, and external data bought from various vendors to enriched the knowledge base. The completeness criteria measures if all of the information about entities under consideration is available and useable.
2. Inconsistent & Inaccurate Data: Consistency measures what data values give conflicting information and must be fixed. It also measures if all the data elements conform to specific and uniform formats and are stored in a consistent manner. Inaccurate data either has duplicate, missing or erroneous values. It also does not reflect an accurate picture of the state of the business at the point in time it was pulled.
3. Lack of Data Lineage & Auditability: The data framework needs to support audit-ability, i.e provide an audit trail of how the data values were derived from source to analysis point; the various transformations performed on it to arrive at the data set being considered for analysis.
4. Lack of Contextuality: Data needs to be accompanied by meaningful metadata – data that describes the concepts within the dataset.
5. Temporally Inconsistent: This measures if the data was temporally consistent and meaningful given the time it was recorded.

What Business Challenges does Poor Data Quality Cause…

Image Credit – DataMartist

Data Quality causes the following business challenges in enterprises:

1. Customer dissatisfaction: Across industries like Banking, Insurance, Telecom & Manufacturing, the ability to get a unified view of the customer & their journey is at the heart of the enterprise’s ability to promote relevant offerings & detect customer dissatisfaction. Currently, most industry players are woeful at putting together this comprehensive Single View of their Customers (SVC). Due to operational silos, each department possesses its own siloed & limited view of the customer across multiple channels. These views are typically inconsistent, lack synchronization with other departments, & miss a high amount of potential cross-sell and upsell opportunities. This is a data quality challenge at its core.
2. Lost revenue: The Customer Journey problem has been an age-old issue which has gotten exponentially more complicated over the last five years as the staggering rise of mobile technology and the Internet of Things (IoT) have vastly increased the number of enterprise touch points that customers are exposed to in terms of being able to discover and purchase new products/services. In an OmniChannel world, an increasing number of transactions are being conducted online. In verticals like the Retail industry and Banking & Insurance industries, the number of online transactions conducted approaches an average of 40%. Adding to the problem, more and more consumers are posting product reviews and feedback online. Companies thus need to react in real-time to piece together the source of consumer dissatisfaction.
3. Time and cost in data reconciliation: Every large enterprise nowadays runs expensive data re-engineering projects due to their data quality challenges. These are an inevitable first step in other digital projects which cause huge cost and time overheads.
4. Increased time to market for key projects: Poor data quality causes poor data agility, which increases the time to market for key projects.
5. Poor data means suboptimal analytics: Poor data quality causes the analytics done using it to be suboptimal – algorithms will end up giving wrong conclusions because the input provided to them is incorrect at best & inconsistent at worst.

Why is Data Quality a Challenge in Enterprises

Image Credit – DataMartist

The top reasons why data quality has been a huge challenge in the industry are:

1. Prioritization conflicts: For most enterprises, the focus of their business is the product(s)/service(s) being provided, book-keeping is a mandatory but secondary concern. And since keeping the business running is the most important priority, keeping the books accurate for financial matters is the only aspect that gets most of the technical attention it deserves. Other data aspects are usually ignored.
2. Organic growth of systems: Most enterprises have gone through a series of book-keeping methods and applications, most of which have no compatibility with one another. Warehousing data from various systems as they are deprecated, merging in data streams from new systems, and fixing data issues as these processes happen is not prioritized till something on the business end fundamentally breaks. Band-aids are usually cheaper and easier to apply than to try and think ahead to what the business will need in the future, build it, and back-fill it with all the previous systems’ data in an organized fashion.
3. Lack of time/energy/resources: Nobody has infinite time, energy, or resources. Doing the work of making all the systems an enterprise chooses to use at any point in time talk to one another, share information between applications, and keep a single consistent view of the business is a near-impossible task. Many well-trained resources, time & energy is required to make sure this can be setup and successfully orchestrated on a daily basis. But how much is a business willing to pay for this? Most do not see short-term ROI and hence lose sight of the long-term problems that could be caused by ignoring the quality of data collected.
4. What do you want to optimize?: There are only so many balls an enterprise can have up in the air to focus on without dropping one, and prioritizing those can be a challenge. Do you want to optimize the performance of the applications that need to use, gather and update the data, OR do you want to make sure data accuracy/consistency (one consistent view of the data for all applications in near real-time) is maintained regardless? One will have to suffer for the other.

How to Tackle Data Quality

Image Credit – DataMartist

With the advent of Big Data and the need to derive value from ever increasing volumes and a variety of data, data quality becomes an important strategic capability. While every enterprise is different, certain common themes emerge as we consider the quality of data:

1. The sheer number of transaction systems found in a large enterprise causes multiple challenges across the data quality dimensions. Organizations need to have valid frameworks and governance models to ensure the data’s quality.
2. Data quality has typically been thought of as just data cleansing and fixing missing fields. However, it is very important to address the originating business processes that cause this data to take multiple dimensions of truth. For example, centralize customer onboarding in one system across channels rather than having every system do its own onboarding.
3. It is clear from the above that data quality and its management is not a one time or siloed application exercise. As part of a structured governance process, it is very important to adopt data profiling and other capabilities to ensure high-quality data.

Conclusion

Enterprises need to define both quantitative and qualitative metrics to ensure that data quality goals are captured across the organization. Once this is done, an iterative process needs to be followed to ensure that a set of capabilities dealing with data governance, auditing, profiling, and cleansing is applied to continuously ensure that data is brought up to, and kept at, a high standard. Doing so can have salubrious effects on customer satisfaction, product growth, and regulatory compliance.

References

[1] Monica Rogati “The AI hierarchy of Needs” – https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007