From the Blog

An icon for a calendar

2019-10-09

Preparing for Data Preparation

With the explosion in data volume and variety, data exchange has evolved from a single action to a sequence of steps for sending data from one place to another. Business value eco-systems simply won’t transact and execute operations unless the data and systems that represent the value is properly managed. And the better it’s managed, more efficient business prospers.

As data becomes more complex, systems and organizations dependent on data exchanges need to deconstruct the complexity by understanding each individual step of data exchange, and that begins with data preparation.

What is Data Preparation?

Data preparation is the process of identifying, cleansing, formatting, and transforming raw data into defined data sets that are further used for data integration, data analytics, and data science. Data preparation is an important set of capabilities for modern organizations, especially when dealing with data that is unstandardized, unstructured, and unformatted.

In absence of data preparation, data scientists and business intelligence analysts are stuck with data that’s poor quality, riddled with inconsistencies, and misrepresented. Business decisions based on skewed underlying data are not career-enhancing to say the least.

In essence, data preparation takes your data and makes it more reliable for gathering insights, and that’s done by passing data through the following 4 stages:

  1. Discover – The first step of data preparation is identifying the data for processing. This step forms the usability definition of the data and how it will be used once data preparation is complete. Data understanding, potential use, and connecting trends are some of the things that are identified in this stage, ultimately setting in motion the remaining steps.
  2. Cleanse – Once the data to be prepared is discovered, the next step is to cleanse and enrich it. Data sets are analyzed, faulty data is flagged and/or removed, and gaps are filled. The focus in this step is on improving the data quality, which is essential for arriving at data that is both accurate and readily usable for actionable insights.
  3. Structure – This stage is about better understanding and structuring the data. This step has a lot to do with the data about the data, i.e., the metadata. Metadata is collected and the semi-prepared data is mapped for technical definitions, relationships within the data, recommendations, and desired mapping formats.
  4. Transform – As this stage, cleansed and structured data is transformed into information formats that are subsequently stored in a data warehouse, injected into business applications or delivered to external business for partners for further processing. At the end of this step the data is ready for use.

Data Preparation And Preparing for a Better Future

Data preparation, although extremely important for getting actionable data and keeping business on the move, can be one of the most frustrating processes of an IT department. And it’s a love-hate relationship every data scientist must embrace. Almost 76% data scientists feel that way, at the same time admitting the vitality of the task to the sanctity of data.

There are several trends in the realm of data preparation that can significantly change the way that you do business. They have big implications on customer experiences, speed to revenue, the cost of operations, and the agility of IT and business to strike a better balance in collaboration and governance.

Self-Service

Many businesses are automating the process of data preparation with a self-service approach, so that your customers and team members can spend less time preparing data and more time getting business done. By effectively reducing this time by up to 80%, you can empower your business to accelerate time to revenue and deliver an impact by making you easier to do business with. That’s because a self-service approach doesn’t just mean that you’re creating a portal for everyday requests for mapping back into the IT department. Rather, it’s a more holistic self-service approach whereby many of the previous business-to-business integration steps are automated and non-IT business people can establish connections with new customers, onboard their data (including data preparation automation!), access dashboards, and monitor business transactions.

The key is in the underlying enabling technology and your ability to reimagine multi-enterprise data eco-systems from a customer experience point-of-view. This is not a shift from EDI to APIs, rather this is a shift from IT-centric connection provisioning and data mapping to a true citizen integrator execution where IT remains in a data governance role.

Machine-Learning Assisted Data Prep

In this era of artificial intelligence, data preparation isn’t far behind in leveraging machine-learning. By analyzing existing data sets and data rules, AI-based data preparation tools can learn and train to perform data preparation for similar data sets.

As a result, AI-powered data preparation tools significantly reduce time and effort of your IT staff by making the process fast and efficient. Businesses can generate quicker return on investment, accelerate time-to-market, and reduce project costs.

See how Adeptia can help you in your quest to better enable data preparation to be a source of competitive strength in your business.