Blog article
See all stories »

Is Your Data Ready for AI?

We’ve already figured out that AI has an immense potential to enhance business processes of many kinds in almost any industry imaginable. AI is poised to redefine conventional business models, enhance productivity, and drive value overall. When deploying it, companies tend to stick to a traditional scenario: first, outline an elaborate strategy, then attract excellent talent, secure the budget, develop a PoC, and so on. However, artificial intelligence experts argue that this traditional roadmap is missing one integral component of successful AI adoption. Namely, data readiness. In fact, even when data gets enough attention, it still remains a solid roadblock on an already thorny path. 

The common trap that organizations tend to fall into is to assume that large amounts of data imply it’s usable. In reality, most data that have been collected without solid governance principles can’t be fed into AI algorithms. Data becomes useful only when it’s properly cleansed, labeled, and structured. Contrary to popular opinion, it’s usually a bad idea to purchase datasets from other vendors as in most cases each company requires its unique data to extract maximum value.

These are a few steps that companies can make to prepare their data for AI implementation. 

 Clarify your goals

First things first, you need to clearly outline how exactly AI will be used and what business areas it will tackle. Although it seems evident at first sight, AI use cases predetermine the datasets to be used. This, in turn, sufficiently narrows down the size of data you will need to audit.

Next, you will need to detect where exactly these datasets are located and who has access to them. For organizations that haven’t yet streamlined their data management, it’s common to have the same type of data stored in different places. Get to know the people who have access to those locations and assess the frequency with which this data needs to be collected and updated.

 Assess the quality of selected datasets

By diving deeper into the process mentioned above, you might also gain a better understanding of the internal issues with your data infrastructure. This will give you a better grasp of the areas of data management that should be prioritized for data quality evaluation.

There are many factors that comprise data quality, including data accuracy, consistency, completeness, validity, timeliness, uniqueness, and other. For now, let’s focus on the first three:

Accuracy. As straightforward as it is, data needs to be correct. Collected data may poorly reflect the real-world situation in many ways. For example, it’s not uncommon for bots to register on websites for a whole lot of different reasons, which can seriously skew your website traffic statistics. Also, if humans are responsible for data input, consider data inaccuracies as a given. To mitigate all of this, look into task automation.

Consistency. One of the most common reasons for inaccurate data analytics is rooted in inconsistent data formatting. For example, when information doesn’t adhere to a specific format, some datasets can easily be missed during filtering, which in turn leads to flawed output. Figure out the established formats and check the chosen datasets for inconsistency.

Quite obviously, it’s easier to maintain data consistency by ensuring that data is entered in the correct format right away. This is why it’s important to set standards for each data point input within CRMs, employee portals, and other systems.

Completeness. This attribute of data quality refers to the comprehensiveness of available data. It’s usually measured as a percentage of data that has found its way into the database. If a client has left some questions in the survey unanswered, it would directly influence the data completeness. Respondents tend to omit some questions in surveys for different reasons (for example, some people don’t like to reveal their age), but it always makes other data they provide less actionable, since the picture of your target audience becomes incomplete.

Most importantly, you should realize that assessing data quality is a continuous process rather than a one-time initiative. The business context keeps changing, and data should keep up accordingly.

Build a data architecture

Data management gets streamlined when enterprises establish a set of rules and standards that define how data is collected, used, stored, and managed.

Essentially, data architecture is all about simplifying data management. It allows companies to cut costs and ensure data integrity. As soon as a data point is created, it should be immediately cleansed, labeled, and properly integrated into the company’s database. This makes all the incoming data ready for feeding into AI algorithms. In regard to data architecture, it’s crucial to define where exactly automation will be at its most effective. Ideally, you want to have a dedicated data solution architect to carry it out.

Closing thoughts

Even after taking the steps provided above, you will most likely end up with imperfect

data for AI, as the latter remains a largely untamed beast. Given the multitude of variables that comprise the readiness of your particular data for a specific AI algorithm, you need to be up for continuous data management readjustments.

One thing that can make this process less painful is to encourage collaboration between data scientists and business users within your organization. Leverage the latter’s experience of handling their data paired with the technical proficiency of your data science team.

Undeniably, high-quality data is a cornerstone of the success in any AI-centric initiative. Expertly built models won’t ever reach its maximum potential with poor data. Nowadays, many data preparation tools can help you save time and get your data ready to a certain degree. However, when it comes to data management, these self-service tools can’t offer highly demanded customizability.

In most cases, each business, big or small, needs a personal approach to data governance. It’s very tempting to cut costs on something you can theoretically do yourself, but it will inevitably result in underperformance. Hire experts that will study your business model in-depth and suggest appropriate data management strategies.



Comments: (0)

Yaroslav Kuflinski

Yaroslav Kuflinski

AI/ML Observer


Member since

17 Apr 2020



Blog posts


This post is from a series of posts in the group:

Business Knowledge for IT

This community aims to provide links, resources, book suggestions, tips and insights to facilitate learning and development of IT professionals in financial services, and to develop a forum for IT professionals to exchange views on various related items.

See all

Now hiring