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Data preparation has always been challenging, but over the past few years as companies increasingly indulge in big data technologies, data preparation has become a mammoth challenge threatening the success of big data, AI, IoT initiatives.
Unlimited data, but limited capacities have led enterprises to use data lakes – a new technology that stores all your data in its natural format.
Unlike data warehouses where the data is cleansed, prepared then stored, data lakes store data in its original form; unprocessed, unprepared, untouched.
In this piece, we’ll specifically talk about data preparation as the most critical challenge and how an ML-based data preparation tool or software can make it easier to process data in the data lake.
Let’s dig in.
Note: Freel free to read this piece on data lakes and data
ingestion challenges if you're not familiar.
Data preparation refers to the process of making raw data usable. It involves cleaning, parsing, deduping, and packaging data for use in
a business objective. Because data lakes acquire data in its natural state
(which could be semi-structured or unstructured), it needs to be ‘prepared,’
before it can be used for insights, intelligence or business strategies.
According to O’Reily, data lakes are designed for business users, however, in my experience, data lakes make it even more difficult for business users to use the data for its intended purpose. For instance, users are required to be proficient at programming interfaces such as Python, R or Spark to work with data in the lake. Worse, companies tend to let their data lakes grow into data swamps where it becomes obsolete and no longer serves the purpose it was intended for.
Enterprise organizations then resort to hiring in-house teams comprised of data scientists and analysts who end up spending 80% of their time in preparing the data for use. While data preparation is a significant part of a data analyst’s job, it shouldn’t be the only focus. Moreover, not all data
analysts or scientists have programming skills to write code and load the data without waiting for IT.
Lastly, the four Vsof big data – volume, variety, velocity, and veracity make it impossible for companies to hang on to traditional methods. Companies receive petabytes of data daily that become wasted within a matter of months because of the rapid rate of data decay. Put simply, if a company is investing in big data and wants the investment to generate ROI +profitability, it must also invest in data preparation solutions that can help them clean, prepare and process this data in real-time without relying on IT or dedicated data analyst.
Without smart investments in the combination of tools, human resources and processes, a data lake is just another component in a company’s list
of digital failures.
Let’s examine how an automated solution overcomes these
challenges in more detail.
While there are dozens of data wrangling/data preparation tools out there, the most effective tools are those that are designed to be self-service –meaning it should be simple enough for a business user to point, click, act. The user must not be required to know any additional programming
language, the training must be easy, and the solution must meet modern data demands.
Additionally, the solution must tackle challenges as:
1. The Limited Involvement of the Business User: It’s often the business user that data lakes benefits. For instance, firmographic data helps with customer journey mappings, lead generation, persona creation etc – all of which are business operations. Why then should the processing and extraction of this data be in the control of IT?
This is the biggest hindrance that prevents most firms from truly benefitting with big data technologies. Most data wrangling solutions that were designed to manage data lakes are so complex that they require experts in a certain technology.
Most even demand users to be certified to use the tool. Business users are left high and dry, constantly either relying on IT users to generate reports or on data analysts to create insights – without ever really studying the data themselves.
2. Making Complex Procedures like Data Cleansing, Parsing, Matching Easier: While writing codes to manipulate data remain a preferred method, it is ineffective and time-consuming especially for unstructured data in a data lake. For instance, a simple operation like standardization (ensuring consistent format across all columns and rows such as all first and last names must be in caps).
Typical data quality problems within a data lake. Incomplete, inaccurate information, coupled with duplication makes this data unreliable and unusable.
Self-service solutions make it easier to process data in the data lake. These
solutions can be integrated into the data lake where data can be cleansed and parsed as soon as it enters the data lake. Or, it can be used to process chunks of the data as is required by the user. In either case, it saves a considerable amount of time in processing unstructured data via manual methods.
3. Processing Data Before it Decays: We’ve already established the fact that data decays at a rapid pace. Assuming it takes a month for a data analyst to clean, parse, and dedupe a data source consisting of a hundred thousand rows of data, the data will have already gone decayed. New incoming data will have to be in the queue for processing for another month. The slow pace of data processing escalates the decay process making it difficult for the business to get accurate, real-time insights.
An example of the cost of data decay for a B2B business.
While there are many data preparation tools out there that aim to address these challenges, only the top-in-line solutions are machine-learning based and self-service at the same time. These solutions make it easier to integrate, profile, clean and process data, allowing business users
to be part of the solution as opposed to relying on IT to perform basic
Of course, a self-service solution is not the one-stop answer to the challenges of data at scale. Companies will have to invest in human resources, develop data-driven cultures, create processes and enable the emphasis on the quality of data. Currently, businesses are so focused on obtaining data that they often forget about the work that needs to be done after the data is acquired.
Very few organizations have a plan that encapsulates the demands of modern data. Hence, it’s imperative that organizations focus on implementing a plan that takes into consideration smart software investments as well as human resources – one is only as good as the other.
Instead of focusing on acquiring more data, organizations must focus on ensuring the quality of their existing data. They must consider enabling their data analysts and scientists with data preparation solutions that can simplify complex processes allowing them the time to focus on obtaining insights as opposed to consistently fixing dirty data issues.
Lastly, organizations must remember that more data is not always better. Less, but high quality data that serves its intended purpose will always be more efficient than more, but useless data.