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INPUT by Alteryx Blog

Accelerate Digital Transformation with Automated Analytics

Automated analytics is the fuel for your organization’s growth, keeping it relevant, agile, and one step ahead of your competitors.

Red roller coaster shooting upwards

Change is inevitable, change is constant, and for business, change can be scary. However, rather than hide from this eventuality, run towards it and embrace it!
Just when we thought things were on the mend, the COVID-19 pandemic continues to rage on. Do you remember what things were like prior to January 2020? Stocked shelves, no masks, and a sense of normalcy. The pandemic accelerated adoption and use of artificial intelligence, analytics, and automation technologies. In fact, Frito Lay had five years of digital plans that were completed in six months!
Beyond the pandemic, change can result from:

  • Evolving market conditions and customer habits
  • Disruptions and modifications across the supply chain
  • Technological innovations
  • Unexpected events like pandemics and natural disasters

All of these can push businesses to adapt quickly; the world is moving fast, and organizations must keep up. Access to quality data, analytics, and automation technologies help businesses track these various changes and evolve to meet growing needs.
The importance of data cannot be overestimated. TechJury gathered information about the state of big data, and it’s shocking:

  • Internet users generate approximately 2.5 quintillion bytes of data every day.
  • 95% of businesses recognize the need to manage their unstructured data and make it a part of their business.
  • 97% of organizations are accelerating investments in artificial intelligence (AI).
  • The market for analytics from big data is going to touch the $103 billion market by the year 2023.

These are enormous numbers — mind boggling, especially considering that 90% of the data in the entire world has been created in the last two years.
With the explosion of data — both structured and unstructured, a new paradigm is needed. Let’s look at some of the problems with traditional approaches to analytics.

Challenges with Traditional Forms of Analytics

The Static Nature of Traditional Analytics
Traditional forms of data analytics depend heavily on dashboards and visualizations. These dashboards are created based on predefined, common business-related questions. To arrive at a response for a new question means a lot of time and technical prowess by a data analyst. Such dashboards cannot adapt swiftly to the changing needs of a business. New challenges don’t often provide a business with the luxury of time.
Traditional Analytics are Inherently Biased
In traditional analytics, dashboards work on a pre-defined set of ideas. These are representative of a specific viewpoint that the business holds. The dashboards tend to be inherently biased because they only display what is pre-determined as important and reflect historical opinions and ideas. Only the data that supports this inherent bias will be displayed. Answering any question posed will be based on hypotheses that are input by an individual, therefore subjecting it to their experiences and bias.
Traditional Analytics Present Facts but Can’t Interpret
Dashboards can tell you sales figures for the year (and even each quarter or month). But, if the sales figures have changed, the dashboard won’t be able to interpret the information and tell you why this has happened. Instead, the information is downloaded and then sorted and filtered based on a range of hypotheses to arrive at an answer.
This is when data analysts or scientists should come into the picture.
Organizations and business executives need to have answers to formulate action plans quickly so they can capitalize on what their data is telling them. With the more traditional forms of analytics, the analysis timeframe is drawn out, and businesses don’t have the time or professionals to execute the analysis. Many tend to skip the analysis or work with skeletal information and results. This guessing game is the very thing that analytics is designed to avoid — making decisions based on anything other than concrete data.
Lack of Real-Time Analytics
Traditional analytics generally take place after the occurrence of an anomaly or problem. It does not happen in real time, meaning the organization only finds out after the fact, not as it happens. Imagine a credit card being used several times by a thief before the bank (or the user) notices. Being advised of anomalies in real time is important.
Distributed Architecture
Traditional analytics typically use data stored on a centralized architecture-based database. Modern-day analytics store data in the Cloud, enhancing security and accessibility for real-time analytics.
Multiple Sources of Data

Traditional analytics depend on a limited number of data sources, whereas modern forms source data from multiple systems and inputs. System architectures such as microservices have a distributed set of databases to collect data from, and traditional analytics may struggle to manage multiple sources across a range of languages.

Benefits of Automating Analytics

With analytics automation, a business can eliminate all legacy-related processing problems and deliver the right insights to the right person or system at the right time. Analytics automation is about focusing on a human-centric approach to data analysis and related insights. It enables a smoother flow of data and makes room for the democratization of analytics. With automated workflows, the mundane, boring, and repetitive tasks are eliminated. This saves both time and effort. It also allows employees easy access to insights. This sets the base for a business to develop into an agile organization. There are a range of other benefits of automated analytics.
Faster Execution of AI and Data Science Projects
The biggest plus point to automating analytics is the use of artificial intelligence to help speed up data science projects. By assigning a machine to the repetitive, time-consuming tasks essential to the process, data scientists can enhance their own efficiency. The output is arrived at faster and can be analyzed in a smarter way. A data scientist can work on several projects, doing more in lesser time.
Increased Accuracy
Automation also removes the chance of human error. No data entry errors, or problems with manual formulas or processes.
Enhanced Productivity
The time a data scientist saves by assigning work to an automated system can be used to work on value additions to the project. By handing repetitive tasks over to a machine, the humans involved can focus their energy and time on creative, complex tasks. These complicated tasks are typically the high-value additions to a project or organization.
By removing boring tasks from the humans and reallocating them to machines, it makes work more rewarding and interesting.
Augmenting Data Scientists
When a data analyst or scientist is provided with the power of automating analytics, it not only allows better efficiency but increases the number of ways how data can be interpreted. It gives the scientists insights of higher quality, giving them more in-depth knowledge of the particular aspect of the business they are focusing on.
Increasing Accessibility to Data Science

With automation of analytics, the democratization of data science takes place. It makes it easily accessible to even those who are new to the field. To work with algorithms, it will no longer be necessary to have advanced knowledge of statistical and computer programming. In the long term, this may even help to deal with manpower shortages.

Best Practices for Automating Analytics

To get the best outcomes from automated analytics, there are some steps to take in the planning process.
Ensure the Right Questions Are Being Asked
It is important to evaluate an analytics automation project before starting and ensure that it will be effective. Does the right data for this exist? Is this measuring what we think it is? Can the results be replicated across other departments? Remember the Pareto principle; is this worth doing?
The Right Leadership
Automation projects can fail if all the stakeholders are not on board with the project. While there are many contributing factors to this, a lack of effective leadership can exacerbate this. Having the right people at the helm (and the right number of them) can improve the efficacy and success of implementation of an analytics automation project. 
Ensuring Maximum Return on Investment (ROI)
Companies often measure the success of a project based on the return on investment. Organizations can erroneously take a short-term view, finding the start-up costs of automation high and opting out of it or only for partial implementation. Taking a longer-term view can show a positive ROI to alleviate concerns, or showing how implementation can be replicated across departments.
Also consider the impact that is less measurable; the reduction in human error and increased accuracy.

Big Benefits from Big Data

The automation of analytics takes the swathes of data an organization collects, and turns it into gold. It takes impenetrable processes forged by geeky data scientists in the basement, and turns it into data that’s accessible to all, with employees, managers, and executives being able to see what they need to. It takes complex data and generates insights that can propel an organization forward.
And, if you’re not automating your analytics already, know that your competition is. So what are you waiting for?

Read This Next

Check out our free eBook Automating Analytics today.

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