Rise of Automated Analytics: Benefits & Challenges

From consumer electronics and automatic cars to pre-approved payments for subscriptions, automation is ingrained in our daily lives. It improves productivity, saves considerable amounts of time, and reduces manual efforts in doing repetitive tasks. With organizations focusing on implementing digital transformation and improving operational efficiencies, there is an increasing need for widespread automation across business processes. In its top strategic technology trends for 2022, Gartner lists Hyperautomation, a business-driven approach to identify, vet and automate as many business and IT processes as possible, as one of the top trends.

When it comes to data analytics, there is a lot of scope to automate processes that analysts still struggle to do manually. By automating data analytics, organization become efficient in managing and processing data for generating insights.

What is Automated Data Analytics?

Automated Data Analytics refers to the process of automating various analytics tasks using computer systems without manual intervention or efforts. These tasks can include data-related processes such as collecting and managing data, preparing data for analysis, implementing governance policies, and selecting machine learning models. Automation can also be brought in to specific tasks such as monitoring customer engagements, tracking specific business metrics, auto-updating data in dashboards, and sending alerts and notifications.

Automated data analytics benefits both the data analysts who sort, prepare, and manage data, and the business users who use it to gain business insights. Data analysts can automate routine and repetitive analytics tasks, thereby saving time and efforts. For example, analysts can use automated data catalogs to classify data, enrich metadata, identify relationships, and organize it in a meaningful way.

Business users can set preferences to automate the way they receive and use business insights. For example, they can set the metrics they want to track and receive automated headlines on insights as they happen in data without searching for them. Similarly, they can automate refreshing weekly sales dashboards and always get the latest data without refreshing them manually every week.

Top Benefits of Automated Data Analytics

Implementing automated data analytics can help organizations gain the following benefits.

Increase speed of onboarding and processing data

Automated data analytics make the most tedious and time-consuming stage of analytics easy and fast – curating data. It involves identifying elements and attributes in data, discovering entity relationships, and evaluating the quality of data. An automated data catalog facilitates quick onboarding of data, enriches metadata, and categorizes data in an easily understandable manner. This increase of speed in processing data ensures that users receive insights from the latest available data. Also, as data continues to grow, the scope of analysis can be easily expanded to include new data sources and more datasets with automated data analytics.

Identify data observability issues

Data can often be messy, inconsistent, inaccurate, incomplete, and outdated. This directly affects the quality of insights. So cleaning it becomes an important task while processing data. With an automated data catalog, the time required to identify data quality issues reduces significantly from weeks to hours. The automated data catalog measures quality in terms of completeness, clarity, integrity and other such parameters and gives complete visibility and recommendation to tackle any data observability issues. This improves the accuracy of search results and ensures that users receive consistent and trusted insights from their data.

Accelerate time-to-insights

Faster data processing leads to faster generation of insights. Advanced analysis such as root cause analysis and trends prediction can be automated to generate results in a matter of seconds, instead of days. With automated data analytics, personalized insights can be sent to users in an automated way in the form of business headlines. These can be generated and sent as they happen in data, instead of waiting for users to search for them. This ensures that users are always aware of the latest happenings in their business and have actionable insights at their fingertips for taking data-driven decisions.

Save time and costs

By automating time-consuming tasks in the process to generate insights from raw data, valuable hours of time are saved for all parties involved – data scientists, engineers, analysts, administrators, and of course business users who consume insights. Data analysts can scale their data processing capabilities with automation. Administrators can keep checks on data governance with granular access policies. Business users can take faster decisions with automated business headlines and advanced analysis. All of these activities that require days or weeks if done manually, can be done in minutes or hours with automated data analytics.

Improve efficiency in decision-making

Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated. Automation can improve the speed of decision making. Routine decisions can be automated, leaving decision makers to focus on high-impact, high-priority ones. Business users always want to understand the bigger picture but the investigation and analysis requires a lot of efforts and time. With automated data analytics, they can get the analysis done in an effortless way to understand “What, Why, How” of business data. With instant access to actionable insights and automated recommendations, users can become efficient in making confident decisions.

Reduce potential of errors

Automated data analytics eliminates the guesswork, errors, and biases that can creep in manual processing, especially in high-pressure situations. It enhances users’ ability to process huge volumes of data in a short time to find the right insight. It provides the much required support for accurately interpreting business situations and identifying the right drivers, thereby reducing possibilities of human errors.

Improve productivity and innovation

Cleaning and organizing data takes up majority of the time of analysts. As data continues to explodes, datasets grow larger and messier, burdening analysts further with time-consuming tasks. This leaves them with very less time to actually mine and model data for insights. Automating analytics tasks frees up data analysts to focus on complex use cases. They can utilize the saved time and resources to work on innovations, better solutions, and other productive activities.

Right Time to Automate Data Analytics

When to automate analytics depends on the objectives an organization is trying to achieve – improving efficiency and productivity of analysts, saving time and costs on analytics activities, or increasing data processing capabilities for expanding business.

The following are some tasks which can be considered for automation:

• Onboarding data instantly by discovering, curating, and categorizing entities, attributes, and relationships to save manual data preparation and massaging efforts
• Enriching data for friendly names, data types, synonyms, default values, and other such details to reduce data entry and massaging efforts
• Identifying missing, incomplete, or inconsistent data values and receiving recommendations on fixing data observability issues to save manual data cleaning efforts and delays in addressing issues
• Monitoring data constantly for specific business metrics and identifying influencing drivers to highlight opportunities or threats in time
• Sending automated business headlines on anomalies, analogies, clusters, and trends in data without users searching for them
• Generating personalized and contextual insights based on the user’s role, work area, or interests to support decision making
• Generating signals or related searches along with answers to original question to save user the time required for querying further questions
• Performing advanced analytics such as root cause or why analysis and trends prediction in minutes instead of days
• Automating repetitive tasks such as updating charts and dashboards at regular intervals based on set schedules

Challenges for Implementing Automated Data Analytics

Implementing automated data analytics brings numerous benefits, but it also presents certain challenges that organizations need to address. Overcoming these challenges is crucial to ensure successful automation and maximize the value of data analytics. Here are some key challenges organizations may face when implementing automated data analytics:

Data quality and integrity

The quality and integrity of data are vital for accurate and reliable insights. Automated data analytics relies heavily on data inputs, and if the underlying data is incomplete, inconsistent, or of poor quality, it can lead to erroneous results. Ensuring data quality through data cleansing, validation, and integration processes becomes essential before automation.

Data governance and privacy

With automation, there is a need for robust data governance practices to maintain data privacy, security, and compliance with regulatory requirements. Organizations must establish policies and procedures for data access, usage, and retention to protect sensitive information while enabling automation.

Scalability and integration

As organizations grow and accumulate more data, scalability becomes a challenge. Implementing an automated data analytics solution that can handle large volumes of data and integrate with existing systems and technologies seamlessly is crucial. Scalability considerations should encompass both processing power and storage capacity.

Skillset and change management

Automated data analytics requires a skilled workforce capable of leveraging the technology effectively. Organizations may need to upskill or reskill their employees to adapt to the new automated workflows and tools. Change management efforts are also necessary to ensure smooth adoption and acceptance of the Automated analytics processes across the organization.

Model selection and adaptability

Choosing the right machine learning models and algorithms for automated analytics can be challenging. Different use cases and data types may require specific models, and selecting the most appropriate one is essential for accurate insights. Moreover, models need to be adaptable and flexible to accommodate evolving business needs and changing data patterns.

Transparency and explainability

Automated data analytics often involves complex algorithms and models that may produce results that are difficult to interpret or explain. Organizations need to ensure transparency and interpretability of the automated analytics processes, especially in regulated industries or scenarios where decisions impact individuals' lives or rights.

Addressing these challenges requires a strategic approach that combines technological advancements, organizational alignment, and a focus on data governance and quality. By proactively tackling these challenges, organizations can effectively implement automated data analytics and unlock its full potential.

Best Practices for Automating Data Analytics

Implementing automated data analytics requires careful planning and adherence to best practices. By following these practices, organizations can ensure successful implementation and derive maximum value from their Automated analytics initiatives:

Define clear objectives

Begin by defining clear objectives for automated data analytics. Identify the specific use cases, goals, and metrics you want to achieve through automation. This clarity will guide your implementation strategy and help align the Automated analytics efforts with your business objectives.

Prioritize data quality

Invest in data quality initiatives to ensure the accuracy, completeness, consistency, and integrity of the data. Implement data cleansing, validation, and enrichment processes to improve the quality of input data, as high-quality data is crucial for reliable insights and automation.

Establish robust data governance

Develop comprehensive data governance policies and procedures to address data privacy, security, compliance, and ethical considerations. Define roles and responsibilities, establish data access controls, and ensure compliance with relevant regulations. Data governance frameworks provide a solid foundation for automated data analytics.

Choose the right technology

Selecting the right technology platform for automated data analytics is vital. Consider factors such as scalability, ease of integration, compatibility with existing systems, and user-friendliness. Evaluate different automation tools and platforms to find the one that best suits your organization's needs and requirements.

Improving Efficiency with MachEye’s Automated Data Analytics Platform

MachEye’s automated data analytics platform is a powerful solution that can significantly enhance the efficiency of organizations' data analytics processes. By leveraging advanced automation capabilities, MachEye empowers businesses to streamline their data workflows, derive valuable insights, and make data-driven decisions faster. Here are some key ways in which MachEye's platform can improve efficiency:

Simplified data onboarding & Preparation

MachEye's platform offers low-prep, no-prep onboarding by automating the process of discovering, curating, and categorizing entities, attributes, and relationships. This eliminates the manual effort and time typically required for data preparation and cleansing. With MachEye, organizations can quickly access and analyze their data without delays, enabling faster insights generation.

Intuitive data exploration & Visualization

MachEye provides a user-friendly interface that enables business users to explore and visualize data effortlessly. Its natural language processing capabilities allow users to ask questions in plain English and receive instant, relevant insights. By eliminating the need for complex queries or technical expertise, MachEye's platform accelerates the data exploration process and facilitates faster decision-making.

Automated insights & Recommendations

MachEye's automated data analytics platform generates personalized and contextual insights based on users' roles, work areas, and interests. By leveraging machine learning algorithms, the platform identifies patterns, trends, and anomalies in the data and proactively delivers actionable recommendations. This automation of insights generation saves time and empowers users with valuable information for making informed decisions.

Advanced analytics & Predictive capabilities

MachEye's platform goes beyond descriptive analytics by offering advanced analytics functionalities. It allows organizations to perform root cause analysis, trends prediction, and other complex analyses in a matter of minutes. By automating these advanced analytics tasks, MachEye enables faster insights generation and empowers users to uncover deeper insights without the need for specialized data science skills.

Monitoring topics of interest & Alerts

MachEye's platform offers real-time data monitoring capabilities that track specific business metrics and identify influencing drivers. Users can set up automated alerts and notifications to be promptly notified of critical changes or anomalies in the data. This proactive approach ensures that organizations stay on top of the latest developments and can respond quickly to emerging opportunities or threats.

Seamless integration & Scalability

MachEye's platform seamlessly integrates with existing data sources, tools, and technologies, allowing organizations to leverage their existing data infrastructure. It offers scalability to handle large volumes of data, ensuring that the platform can grow alongside the organization's data analytics needs. By eliminating the need for complex integrations or data migration, MachEye simplifies the adoption process and accelerates time-to-value.

Enhanced collaboration & Accessibility

MachEye's platform promotes collaboration across teams by providing a centralized data hub accessible to all stakeholders. It allows users to easily share insights, reports, and visualizations, facilitating collaboration and alignment. With MachEye, decision-makers, data analysts, and business users can work together seamlessly, improving overall efficiency and fostering a data-driven culture.

In conclusion, MachEye's automated data analytics platform offers a comprehensive set of features and capabilities that significantly improve efficiency in data analytics processes. By automating data onboarding, simplifying exploration, generating automated insights, enabling advanced analytics, providing real-time monitoring, ensuring seamless integration, and enhancing collaboration, MachEye empowers organizations to maximize the value of their data and make informed decisions faster.