Data preparation is hardly a novel concept. Ever since companies began collecting and storing raw data from disparate sources, it required essential extraction, cleansing, and transformation functions to turn raw data into a form that can be readily used for business purposes. All these functions were performed by IT experts who were trained in domain-specific languages to manage relational databases and manipulate the data stored in them. SQL is one of the most popular languages allowing IT experts to manage databases like Oracle, Access, MySQL, and other popular databases.
Up until the last decade, IT experts would manually code scripts to make changes in data sources. Data was fully governed by IT and data preparation would be limited to ETL functions (Extract, Load, Transform). Data preparation while important was not important enough to receive the focus that it does today.
How Has Data Preparation Evolved Over the Years?
As data evolved, so did the methods in preparing it. But of recent years, data preparation has received significant focus because of the maturing of big data and the interconnectivity of systems, devices, and apps, powered by the internet. Organizations are required to pursue data-driven innovations if they want to remain sustainable in the future. To fuel this innovation, big data workflows must encapsulate the volume, variety, velocity and veracity of data. Most importantly it must include data quality as a core component in the data management system.
The biggest challenge preventing organizations from truly being data-driven is the inherently unstructured nature of big data. Even a small business today has to deal with large volumes and varieties of data streaming in from multiple sources. Enterprises on the other hand have to ensure the accuracy and integrity of their data to comply with regulatory regulations and customer expectations. Additionally, there is a demand for data to be accessible in real-time. Companies no longer have the luxury of delaying data processing. To meet rising demands, it needs to produce quality data within minutes.
In the current business system, data preparation capabilities are still limited to manual processing. Moreover, it doesn’t allow for in-depth data discovery and neither for meeting the rigid demands of quality, originality, uniqueness and completeness. For IT professionals, this is a challenging time. Speed, accuracy, integrity is difficult to achieve as it is; with big data, it’s almost impossible to achieve with traditional methods.
Why are Traditional Methods No Longer Effective?
Data has become bigger and messier and everyone wants data faster. IT teams are failing to meet with the rising pressure of exponential business cases. Take for instance the case of a retailer we worked with. When the business moved online, suddenly they were dealing with an influx of unstructured data gathered from cookies, third-party platforms, web forms, mobile devices, mobile apps and third-party vendors. The retailer’s IT team could not keep up with the speed at which this data needed to be prepared for their quarterly analysis. It was a whole new challenge that they were unprepared for. The company began a hiring spree bringing in more SQL experts on board, moved to the cloud and even migrated to an advanced CRM – yet, they were struggling to get accurate insights on time.
The business’s IT teams were still using ETL methods to prepare data which while works perfectly well with structured data, is incapable of managing unstructured data. ETL is also more rightly a data warehouse tool used to extract, load, and transform structured data. It cannot be applied on unstructured or semi-structured data stored in a data lake or in cloud storage. Traditional ETL structures struggle to support the agility required by modern, data-driven businesses.
Going back to the retailer’s example, the IT team would spend 3 weeks to just profile half a million rows of unstructured data. Then another month would go by in running scripts to clean, standardize, and aggregate this data. We’re not talking about data matching yet – which is another key function that businesses desperately need to merge lists and data from disparate sources. And data matching via SQL programming neither returns accurate results nor can it cater to the various nuances of modern data structures. This retailer’s IT team spent 3 months in just preparing half a million rows of data – by the time business analysts had the chance to study this data, it was already obsolete and were not reflective of the real-time changes in the retail industry.
Keeping all these challenges in view, it makes sense to propose a new reform for IT professionals – that of self-service data preparation tools. But here’s an important point to remember – these tools are only as good as the data management process and culture of an organization. If the company has an ad-hoc, or a non-existent data management infrastructure, not even the best-in-line tool can save the day.
How Self-Service Data Preparation Tools Can Optimize Efficiency?
Self-service data preparation tools are essentially designed for business users to process data without having to rely on IT, however, that doesn’t mean IT users cannot benefit from an integration of self-service tools with an existing ETL framework.
The whole purpose of self-service tools is to remove the need for manual coding and scripting. This means if a company is not yet ready to let business users work with the data, at least IT users can benefit from zero-code data preparation.
Here’s what can happen when IT users embrace self-service solutions:
- Save Up on Manual Time and Effort: Why waste 2 weeks in profiling when you can get that done in 2 hours? Best-in-class solutions let you profile a million rows of data for over a dozen types of errors (you can also build your own rules and patterns without SQL coding) within just 15 minutes! With the time saved, IT users can better focus on data governance and analysis.
- Get Accurate Data Match Results: Traditional data matching methods never return accurate results. Countless people we’ve spoked with are miserable with the amount of effort they have to put in to verify false positives and negatives after a data match process, so much so that most of them would rather manually verify and match each record in Excel than in using an algorithm or running a script. ML-based self-service data prep tools also allow for powerful data match functions that use a combination of the fuzzy matching algorithm along with proprietary algorithms to deliver highly accurate matches with accuracy rates up to 95%.
- Flip the 80/20 Anomaly: 80% of the time spent in data preparation? Flip the game. With a zero-code solution, IT professionals can spend 80% of their time in analysis and governance and 20% in data prep.
- Share the Burden with Business Users: Self-service solutions can empower business users to prepare data as required, reducing the dependency on IT users. In an age when data drives business operations, business users must be involved. Limiting data to a certain domain or authority impedes any progress towards being truly data-driven.
Self-service capabilities are in demand. These tools allow both IT and business users to prepare and transform data through an easy-to-use interface, with no requirements for knowledge in domain-specific languages. Many of these technologies use machine learning and natural language processing to guide users to work with data, avoiding coding altogether.
As the stakes for data accuracy goes higher, organizations can no longer treat data as a backside process. The stakeholders for data are no longer limited to internal executives, now it includes customers, regulators, vendors, business partners, investors and any other entity that is involved with the organization. Improving data preparation processes, reducing manual efforts while ensuring data consistency and accuracy will help organizations drive into a data-driven future with confidence.