Data analytics in 2023: how AI and automation are implemented
- 1.1 Data preparation
- 1.2 Predictive modeling
- 1.3 Pattern recognition
- 1.4 Automation
- 1.5 Data entry
- 1.6 Data preparation
- 1.7 Data validation
- 1.8 Data transformation
- 1.9 Report generation
- 1.10 Data warehouse maintenance
Technology is rapidly transforming data and analytics, allowing businesses to gain a competitive advantage and fostering new business innovations. Previously, an organization could only access descriptive analytics. As the data generated continued to increase, predictive analytics was introduced.
However, technological advancements have now led to the introduction of artificial intelligence (AI) and automation in the data analytics world. Companies are optimizing their processes efficiently, understanding insight generation, and making decisions quickly by implementing AI and automation in their analytics.
Combination of AI and automation for data analytics
Although human analysts can analyze and interpret data, the process takes time, especially when dealing with large amounts of data. AI can process big data at a fast speed and save time. Implementing machine learning, natural language processing, deep learning and other AI technology in data analytics can also lead to more insightful and accurate results.
Below, we explore how AI and automation are being implemented in data analytics.
Data analytics involves a lot of repetitive tasks that are time-consuming for an analyst. Tasks such as data collection, cleaning, integration, normalization, and feature selection are carried out for every collected data set.
Businesses use AI to collect data through web scraping. This involves using automated scripts or bots that extract information from websites. Through AI, the bots can identify and collect exact data points that a company needs, such as customer reviews or prices.
Data cleaning helps to ensure that the data being analyzed is consistent and accurate. Companies implement AI in data cleaning to identify missing values, correct errors and standardize all data formats. AI also evaluates and verifies data quality across different sources. It can detect data deviations such as trends or spikes to help with accuracy, coherence, and relevance. This helps to strengthen the value of the predictions and ensures reliable conclusions.
Human analysts need to review the data and identify features related to the predicted outcome. This can be a time-consuming process. Companies are implementing AI in feature selection to identify relevant features correlated with the prediction. This makes it easier to remove the irrelevant ones quickly and allows the analyst to focus on the analysis process.
Data visualization is an essential part of the analysis process. Businesses are using AI to generate a visualization of data through heat maps or scatter plots to identify trends.
Businesses must stay ahead of the competition, which involves predicting future outcomes or events. Predictive modeling is a part of predictive analytics that uses data mining and machine learning to forecast consumer behavior, activity, and trends.
Companies dealing with advertising and marketing use predictive modeling to assess historical behavioral data. They then use those insights to plan their marketing campaigns and forecast sales trends. Financial institutions are also implementing predictive modeling to predict their future cash flow. The business can project its expenses, revenue, and sales by using historical data from past financial statements and current data. This allows them to make informed, data-driven decisions.
Pattern recognition involves using a machine learning algorithm to recognize patterns in data automatically. Businesses implement pattern recognition-based security systems to understand user behavior, monitor activity trends, and block hackers. A business user can take precautionary measures before a possible threat when they can easily access malware patterns.
Companies such as Facebook employ pattern recognition to determine fake accounts by using a person’s profile photo. AI can turn large amounts of data into actionable insights.
Traders also utilize pattern recognition to forecast stock markets and make trading decisions. Apple’s Face ID and Touch ID use facial recognition and fingerprints, respectively, which are part of pattern recognition and depend on AI technology to find unique patterns in data.
Data analytics is an iterative and exploratory process. Pursuing an MBA in data analytics from an accredited institution such as Walsh University teaches you in-demand skills such as quantitative research techniques, statistical analysis, hypothesis testing, and research reporting. Skilled data analysts understand how automation can reduce repetitive tasks and provide valuable insights when implemented on various tasks. Below, we look at how automation is being implemented in data analytics.
Companies spend considerable time and resources collecting and inputting data into spreadsheets and other platforms. Businesses are now shifting toward data entry automation with machine learning. This allows them to only input relevant information by identifying specific data points and fields.
Business users employ automation to extract information from unstructured data such as scanned files, images, and documents. Optical character recognition is a popular automation method that companies use to scan and extract data. The technology can convert typed or handwritten text into machine language, which you can configure in your preferred way. OCR can also work with PDFs and images.
Robotic process automation can perform everyday repetitive tasks such as updating databases, filling out forms, and more.
Data entry automation is standard in science and healthcare. It’s used to process the results of multiple experiments into a system and predict medical emergencies. The finance sector also uses automation to machine-read financial documents and reduce errors.
Data analytics involves a lot of repetitive tasks such as data extraction, cleaning, and standardization. Companies are leveraging AI and automation to extract and validate data from different sources such as websites, documents, and emails. The algorithms can comprehend the data context, which helps minimize errors and ensure accuracy in the process.
Businesses are also relying on automation to correct inaccuracies and remove duplications. With this technology, companies can improve data quality and maintain data integrity for analysis.
Businesses are automating data validation to determine data formats that do not match a dynamic data model. Companies are also automating data validation to detect typos and missing values that could affect data quality. Automation streamlines the data modeling process, making it easier for companies to analyze the data better and gain insights.
Being able to convert your data into an analyzable form is something that most companies struggled with in the past.
However, more companies are now using automation platforms to automate data transformation from data extracted from documents. The data is uploaded on the platform and relevant data is extracted before cleaning and merging for analysis.
Automated data transformation is expected in biopharmaceutical companies and healthcare organizations that constantly manage large volumes of unstructured data. Much time is spent structuring and cleaning the data to gain valuable insights. Automation allows companies to extract, integrate and standardize data for easy analysis.
Standard report generation tools such as Tableau and Microsoft Excel streamline the report generation process, minimizing errors and enhancing accuracy. However, companies are now investing in software to automate manual tasks involved in creating reports. These tools can generate reports from data sources such as spreadsheets, databases, and other applications.
The process involves entering a few details, setting up a template, and automatically generating the report on your preferred schedule.
A business can then customize the reports depending on the required parameters for each one. Additionally, you can save the templates in the software, and the system will generate the templates when you need to create automatic reports.
Data warehouse maintenance
Data warehouse maintenance mainly involves data loading, where data is stored and used for analysis; data management, which includes defining data types and tables; and data monitoring to check the data warehouse for issues or errors.
Traditionally, building a data warehouse and maintaining it required manual ETL coding. Unfortunately, the process took time, and the business environment had already changed.
Companies use automation to change their data warehousing models to accommodate the dynamic business environment. This includes automating the design, development, analysis, maintenance, management and any other process involved in the data warehousing cycle. Utilizing automation allows businesses to facilitate data analysis as they save time focusing on the data warehouse’s optimization and design.
The future of AI and automation in data analytics
The need for more skilled data analysts is one of the hurdles that most companies face in 2023 when implementing AI and automation. Businesses are expected to look for different ways to automate the entire data analytics process, including data collection, data visualization, predictive modeling and other workflows.
In the future, companies might switch to no-code AI platforms that reduce data analysis costs and quickly convert raw data into valuable insights. These platforms are helpful for business users without coding skills as they can easily collect data and fine-tune AI models to gather insights and gain a competitive advantage in the market.
Implementing AI and automation in analytics gives businesses a comprehensive overview of their operations, target market, customers, and competitors. Companies get to understand the reasons behind their past failures and predict future outcomes. They can also determine what would happen if they took a different action.
With this technology, companies can identify patterns in customer behavior, predict user behavior, optimize pricing, create effective marketing campaigns, and achieve better business outcomes.