Automating Data Analytics is the best way to save on cost, time & efforts while making informed decisions that are profitable for you & your organisation.

1. Automated Data Analytics: How, When &
Why?
Do you find repetitive and mundane work preventing your data analysts, engineers,
and analysts from performing their best? Then, think about automatizing your data
analytics to let them be free of mundane tasks.
Automated analytics: What are they?
Automated analytics involves the use of computers to produce analytical products
using very little or no human interaction.
Through automating analytics, you create systems that can automate a portion or even
the whole data flow that brings a data-related product to life, from automating
dashboards for business intelligence to self-governing machine-learning models based
on data models.
You can try automated analytics with freemium versions of various tools available in
the market.
What are some examples of automated analysis?
Each step in the pipeline of data may be automated using data analysis as per your
requirements:
1. Data collection. Before you can analyze data, it is necessary to have the data.
However, obtaining the data could be lengthy. Finding the scattered Excel files to
create an app that retrieves data from third-party Apps and gathering the data required
to conduct analysis could be a long process. Automating data collection can help to
speed up the time it takes to deliver an analysis of data. Use software to automatically
collect information from different sources and put them in a timetable to keep the data
you receive current.
2. Dashboards. Imagine what it will take to create an effective dashboard that
monitors the company's KPIs. It is essential to gather the relevant data, then analyze it
to extract metrics (with Excel, Python, R or whatever your preferred language is) and
then display the results on a graph that decision-makers can view. The entire process
from beginning to end is lengthy. Automating scripts to analyze the data or using tools
like Looker or Metabase that automatically display data could reduce the time when
creating dashboards.
3. Business intelligence. The process of establishing business intelligence excellence
requires far more than just dashboards. It would help to look at various metrics and
breakdowns for the various business units to gain new data. Are monthly orders rising

2. within the EMEA region, however not so in APAC? Automate the process of creating
the different breakdowns through an automated method of preparing data. Create
cubes that combine data broken down according to other dimensions in your
warehouse. For instance, the total amount of transactions (aggregation) by geographic
location (dimension). Or the average order size (aggregation) by customer segment
(dimension). You can then take a look at the cubes and gain conclusions from the
automated analysis.
4. Models of machine learning and automation of big data. The experts in machine
learning develop statistical models that surpass humans in a variety of tasks. For
instance, a machine-learning model is far more adept in forecasting which ads will
receive clicks than humans. However, building a predictive click model on its own
won't cut it. Consumers' preferences are changing and what they buy on the internet is
changing too. Unlike data scientists or machine-learning engineers creating models
every couple of weeks, you can automate model creation and selection. With the help
of automation, you can construct diverse models by choosing various parameters
based on diverse data combinations. You can then automatically select which model
has the highest predictive score for clicks and apply it to production. Automation isn't
only for the development of models. Financial institutions and banks employ
advanced anomaly detection algorithms that look for signs that could indicate
fraudulent transactions. When the signal is above an amount, the models initiate an
account audit through alerts sent to human inspections.
The possibilities for automated data analysis are limitless, and that's why you should
take the initiative?
What are the advantages of automated data analysis?
There are four primary benefits of automating your analytics.
1. Increased/ speed of analytics. The time it takes from request to delivery of the
analytic report is reduced when you automate (part of) the process for creating the
analytical report.
2. Reduce time and costs. Automating (part or all of) these pipelines reduce
scientists', analysts' and engineers' working hours. This also means that you have less
to pay for rota-related tasks as computers take care of them.
3. Let your time be used to do creative work. Data experts don't need to work on
tweaking pipelines manually, and they are free to solve the tough business issues and
come up with innovative ways to generate revenue.

3. 4. Enhanced processes and systems. Conducting analytics by hand often requires
complicated procedures. Who do your analysts have to speak to, making a note of all
the requirements for clearing the database before analysis, as well as multiple
coordination meetings to transfer the data across departments and staff. When you
automatize analytics, you can skip those parts that are susceptible to human errors.
When you spot a mistake in the automated processes, you just need to fix it only once.
Automated processes help you create future-proof systems and processes.
What is the best time to automate the process of data
analysis?
Some tasks in analytics are not perfect to be automated. Therefore, before you begin
automating, ensure:
1. The proposed task is of significant significance. Automating the job can either
address the business issue (delayed insights result in missed opportunities) and
improve on the overall financial bottom of the line (the efficiency gains translate into
actual cost savings) or even provide the possibility of business expansion (the
automated analytics uncovers new revenue sources as well as cost reductions).
2. The task that is being considered for the candidate is not a one-time task. If
you create the report once, then there is no reason to automate the process. It's often
because of repetition that we learn what parts of the process are simpler to automate.
For instance, making the identical KPI dashboards every three weeks in succession
turns around the light bulb above our heads. The dashboards utilize similar data
sources, and so automation of data extraction can aid in speeding up the reporting with
help of business intelligence tools.
3. Automation can save time by decreasing the chance of errors. An automated
system usually costs more but it is the best option as it will lessens the chance of
errors that are caused due to operating manually. One can also automate data
validation to detect typos, flag and impute missing values. This type of data analysis
automation not only streamlines data modeling processes, and also enables adherence
to models by automatically transforming data.
4. You're ready to improve continuously. The automated system cannot be perfect
the first time. You must be prepared to continually enhance the efficiency of your data
analytics system for success. There are two implications: (1) you must adopt a growth
mindset in which you think about ways to improve the efficiency of your system. (2)
you must create a set of criteria and monitoring to determine if the system is
performing efficiently.
How do you automate the process of data analysis?

4. The way you implement data analytics will depend on the degree of automation you're
taking into consideration:
1. Partial automation. Partial automation is a way to automate existing processes but
eliminates some of the manual labor. For instance, your team of data analysts would
create scripts that accelerate certain aspects of their work.
2. Final-to-last production. Automation is set up from end to end, and computers
create information products for humans to examine and decide on. Automation, for
instance, produces KPI dashboards or alerts about fraud without the employee
handling anything.
3. Complete automation. Complete automation can make business decisions in near-
real-time with no human involvement. For instance, an AI algorithm automatically
determines if the information is sufficient to buy and sell the assets.
As you progress towards complete automation, the greater the benefit of automation
increases from merely creating time savings to having independent effects on the
company's bottom line.
How do you begin automatizing your data analysis?
1. Find analytical tasks that can be automated.
The ideal task that should be chosen for automation should pass the following
checks:
a) It should have business value
b) Is repetitive
c) Time saving
d) Reduces errors
e) Can optimize the current process further
2. Set expectations through formalizing the guidelines to ensure success. In the
beginning, automation is an opportunity to consolidate processes and reduce time. Be
clear about what you're looking for. Begin with a small task, such as automating just
one process of data pipeline.
3. Make use of dedicated instruments and platforms to accelerate
automation. Your engineers can write the SQL procedures and Python scripts to
automate codes, but using specific tools and platforms can save time in creating
automatized pipelines.

5. 4. Repeat and analyze. As you automatize part of the processes for data analytics and
products, you should evaluate them against the criteria for success that you have set
before. If successful, automate more.
CONCLUSION -
Companies that deal with big data may benefit from automating a portion of their data
analytics infrastructure. Data lakes are stuffed with unstructured data that machines
can analyze faster than humans. In addition, today's data warehouses are characterized
by strict requirements for data modeling and processing, which can easily be
streamlined by automated data analytics.