How Big Data Automation is Changing Data Science?
Big Data automation has seriously impacted both organizational operations and digitalization.
Although this science faced some challenges at its inception, many of these problems have been resolved with increased analytics. But the market is always changing and it’s becoming more challenging to keep up with Big Data and automotive tech. One thing is clear, however - the automation of Big Data is the most complex and powerful challenge data science has ever encountered.
Approach to Automating Big Data
To incorporate big data automation into a broader analytics strategy, companies have to take care of certain foundational tasks. These are:
- - Identify patterns and long-term project values
- - Develop automated processes to recognize these patterns and values
- - Formulate a predictive investigation of the database
Benefits of Automation
There are upfront costs to automating big data. Data technology and advanced analytics are expensive, and automating them carries a cost as well. But companies are deciding to pursue these avenues for a reason. The benefits clearly outweigh the costs. Some of the biggest, immediate benefits include:
- - Long term savings on cost of operations
- - Increased competency across business functions
- - Improvement of self-service modules
- - Increased scalability of Big data operations
Making Automation Work
According to the Institute of Electrical and Electronics Engineers on Data Science and Advanced Analytics, automation depends on the following four things:
1. Time-Varying Data
You want a framework that looks at data over a certain time period. Analytics should be categorized into diverse segments. These segments should be consistently labelled and reflect useful and salient periods, such as quarters, years, or fiscal years.
2. Data Vocabulary
For automation to be effective, a company must establish a coherent and workable set of concept entities. There should be a data dictionary that establishes the vocabulary for certain business concepts. This labelling and classification framework should be logical and involve input from multiple business units.
3. Proper Formatting
The main responsibility of automation is to format data. Once the data is formatted, analysts will be able to review it easily. This makes the presentation of findings much more successful, improving synergy and collaboration between domain experts. This will also ensure that all parts of the organization are using similar classification systems so that shared data is accurate.
4. Production of Self-service Module
The most important aspect of a self-service module is security. Once the data is available and organized, it must be encrypted so that users cannot manipulate the data. Essentially, it must be “read only.” All business owners need to ensure their data processing systems are reinforced so that it cannot be breached. This can be achieved through user authentication as well as audits.
Automation of big data analytics is a massive step toward improving data science ops at your organization. Stakeholders should be able to utilize data without encountering the headaches of deciphering it. This allows analysts to focus on forecasting rather than scrubbing data. Since the ultimate goal is to use the information you collect, it is important that any automation process be geared towards end use such as reporting and visualization for strategy. If the automation process doesn’t increase efficiency, it is worthless, and can't justify the money and effort invested into it.