According to the PWC Global CEO survey, two-thirds of the business leaders believe that data management can be a factor that defines the competitive advantage in the future. Efficient data integration is crucial for successful data management as the accuracy of the data depends on the integration process. Data integration is a complicated, error-prone, and labor-intensive process. Data automation allows enterprises to save money, minimize errors, and redirect employees’ focus on strategic decisions by facilitating data integration processes.
What is data automation?
Data automation refers to optimizing data uploading and delivery procedures using automation tools that eliminate manual work. The traditional practice required manual labor from the IT department to manage and administer data updates on open data portals. Sometimes the responsibility would fall on the employees in different departments that had to handle the data while carrying on their other duties. The manual process is time-consuming and labor-intensive for enterprises. In addition, manual handling of data is error-prone and can affect strategic business insights derived from the data. Hence, data automation is a vital tool for any enterprise looking to upgrade its data integration to a more efficient level.
Do you need data automation in your business?
There are four clues to look for when deciding if you need to automate data automation in your business:
- Repetition: Do your data analysts focus more on repetitive tasks like extracting data from different sources and formatting instead of analysis?
- Recurring errors: Do frequent errors slow down your business analytics?
- Large volume: Do you have a large data entry team handling data integration?
- Urgency: Do you need to speed up your business analytics process?
If your answer is yes to any of the four questions above, data automation could significantly benefit your business analytics, data analysis, and operations in general.
What are the approaches to data automation?
ETL is one of the most common data engineering approaches used by data professionals. According to the procedure, the data automation process includes three steps based on the function of the used tools. These three stages are commonly known by the abbreviation of ETL (Extract, Transform, Load). These stages include:
- Extract – The more your business grows, the more complicated the data entry process becomes. Your data staff may need to extract data from different operation management systems such as ERP, CRM or SQL, and NoSQL Servers, and sometimes even from emails and PDF files. Extracting data from numerous databases manually complicates and slows down the data entry process. Implementation of automation to data extraction significantly reduces the time spent on the process.
- Transform – Automation software transforms the raw data extracted in different forms from numerous sources into a single format for data consistency. The formatting process includes file types such as CSV, PDF, WARC, XML, and content consistency. For example, the data date may be specified as either 20-Sep or 20 September. The automation process eliminates these deviations and even performs necessary calculations to summarize the raw data.
- Load – Automation software inputs the formatted data into the designated data portal in the last stage. The structured data can be used for business analytics or data analysis after loading.
These phases can be completed by different automation software or a single end-to-end data automation software. In fact, enterprises may opt for building their code and pipeline with the help of their data engineers and developers. However, it requires a significant initial investment that reduces decision-making flexibility for the required approach.
Roadmap to Data Automation
- Identify problems: Determine where the repetition occurs and prioritize the data sets based on their added value. It is significant to prioritize the datasets that create the most value for the company as they take more manual effort.
- Define data ownership within the organization: Determine which teams will handle different stages of the data automation process. There are three main approaches to data access and processing within an organization:
- With the centralized approach, the IT team handles the data automation process from A to Z.
- In a decentralized method, each agency processes their data, from extracting the data from source systems to loading them to data portals.
- There is also a combination of the two methods. The hybrid method allows different departments to work with the IT team. IT teams are responsible for loading the data into data portals through a hybrid approach.
- Define the required format for your data transformation: Define the required format for your data transformation. It is crucial to have a set data format policy, to secure data coherence for better insights. Moreover, ETL tools require users to define the preferred formatting of the data categorization.
- Schedule updates: Dataset update allows businesses to make better decisions on their operations. Hence, It is crucial to schedule updates for consistent and up-to-date data for datasets.
- Determine the right vendors for your operations: Businesses can rely on automation consultants’ expertise to help them identify the best vendor according to the business needs and the business structure.
Explore our ETL tools list if you believe your data integration tasks may benefit from automation.
Contact us for guidance on the process: If you need more customized recommendations.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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