How Data Automation is Solving Everyday Problems

Results for the Research Making Decision Using Data.

What are the top pains customers are facing around making informed decision?

1. The lack and quality of the source data.
2. The ability to generate meaningful insights from big
data, don’t have the ability to articulate what they
need to see to make informed decisions.

(For info about big data - go here -
https://www.oqlis.com/big-data/)

3. The complexity of setting up analytics tools to gain
insights, and the amount of the work or time it is
required to skill up business users to self-service.
The default is to export data from databases and
manipulate using excel.
4. Asking the right questions about the data and what
needs to be delivered, people have not defined what
they need to know or what needs to be measured, the
data is only part of answering the questions.
5. Using a baseline to prove improvement for using data as
a way to reduce costs / add value.
6. Access to information
7. Getting the right or correct data, to make the right
decisions. How the data is interpreted or presented.
8. Your data is not telling you a story.
9. Data must be in real time.
10. Deep diving into numerous reports, customers need a
dashboard with a birds eye view to make informed
decisions
11. Collection of data from multiple sources (data input is
not the issue; it is getting the relevant expertise
from the relevant departments together that is the
issue)
12. Getting the relevant skills and expertise to formulate
what needs to be done next, the manpower requirements
and data segregation that take place to make a
decision.
13. Because of a silo approach that is adopted in
businesses, the decision process is hampered by lack of
visibility and makes decision-making time consuming
14. The fact that data resides in various systems,
customers want to have one place to interrogate data
where the UI and UX is consistent (integrated
solution). Discrepancy between data sets causes
friction
15. Very difficult to make informed decisions if you have a
lack of understanding of your objectives and how to
measure these objectives. The customer does not
understand what they need to achieve or what they need
to do on a daily basis with data.
16. Discover your data, to find the gems and diamonds of
insights. Have the ability to convert in a short time
frame and make it easy to people that are not experts.

(View the leader among Data Automation Companies here
https://www.oqlis.com/data-automation/)

What are the items that frustrate you (or your customers) at the moment that you wish you could solve using data?

1. Not knowing the integrity and quality of data.
2. No insights when the data transaction have not been
received or data source or data stream is not
operational (data reliability)
3. Standardization of analytics in a way that it can be
explained, with some flexibility for users to self-
service.
4. Consistency of data presented to customer, in a way
that can be understood.
5. Availability of historic data or data sets, knowing
what data and structure should be stored to add value
at a later stage
6. Accessibility of data that can be explored in a simple
and intuitive way, where a normal business user can
find insights without serious data skills.
7. Sitting with disparate systems or information that is
held within certain individuals, and trying to find the
information from the stored within various sources
8. The time it takes to get information or meaningful
results and (or) tangible outcomes
9. The technical team does not store the data in the
correct way that can be explored at a later time when
require – Disconnect between how data is stored and the
interpretation of data when required.
10. Understanding the area in the business that would be
problematic in the future, when the situation arises it
is too late. Predictive problem solving. (Predictive
cash flow management, predictive sales forecasting
11. Comprehensively of graphs and the explainability.
12. Too much human intervention, it is one thing to have
data but it is another thing to interpret the data that
it supports decision making. In addition, there is not
enough time to get meaningful results.
13. Spending too much time on monotonous tasks, meetings
and emails.
14. Inaccurate and incomplete data, the data does make
sense. To know what the condition of the data.
15. Not fast enough, do not get answers quick enough
16. Value of graphs with dashboards for comprehension
compared to columellar reports
17. Historical data that can predict and extrapolate into
the future

(You might be interested in Artificial Intelligence https://www.oqlis.com/artificial-intelligence/)

So, walk me through what happens when you experience pains and or frustrations using data.

1. Because the data is not easily accessible, or the team
that does the analytics is currently busy with more
valuable tasks, questions and insights are just never
obtained
2. So frustrated, lose interest in the potential insights,
and not answer the question
3. It takes so long to get insights, that by the time the
insights are generated the question or answer is no
longer relevant.
4. Need to understand what is going to happen in the
future, if this is not known the consequences can be
financially crippling.
5. Waste of time, could be using the time for more
strategic items.
6. It takes too much time to proceed / solve - Saving time
on a daily basis.
7. When decision can’t be made due to data discrepancies,
there is a root cause analysis process that needs to be
followed which is very time consuming. This is a
painful and laborious process that incorporates many
parts of the business.
8. The solution becomes useless / unusable

What do you think is the ideal solution to getting the most out of data decision making process?

1. A platform that is reliable and consistent in what it
does for customers
2. Analytics needs the flexibility to allow business users
to do basic analytics without the need for having a PHD
to make daily decisions
3. Provide useful business drivers as outputs
4. Normalises the data, investment in a good data design,
getting the data into something that is useable.
5. Interface that provides ease of use to something
similar to excel but with the power of managing large
data, with some fundamental templates in a standardised
way.
6. Have all your information coming into a centralised
repository, making sure it is organised in a certain
way, and once all the processes are automated have
access to the information using a tool like OQLIS.
Making decision or managing by exceptions.
7. Having access to information at the click of a button,
As a director of a company making decision based on red
flags and signals on a graph.
8. A system that stores the data in a manipulated or
prepared way so that can be easily explored by
customers.
9. A simple system that has a predefined data and time
factors that can present the data in a simple dropdown
way that users can explore their data and gain
confidence in the validity thereof.
10. Managing predictive analytics is complicated, so a
system that manages the entire process. Managing the
models, the algorithms, the models in production and
the infrastructure costs as things change. Addressing
the infrastructure and time requirements for deployment
and integration.
11. Simplified visualisation that tell a story, that
highlights exception/event management to better make
decisions.

So if we can resolve these pains and frustration for your customers, would you think they would find this beneficial for your business?

1. Customer would be more driven by data driven decisions
versus distinctive decisions.
2. Speed, reliability, consistency
3. Quicker deployment and scalability can enable faster
market penetration
4. Lead time to making decisions.
5. Putting the customer in the lead against competitors.
6. Less frustrations.
7. Manage the entire predictive cycle has a financial
benefit to customers – “something in hindsight, is
going to be something in foresight”
8. Supply existing customers with predictive insights will
have a huge impact on engagement, retention and
employee performance.
9. Have more time to meet business objectives.
10. Data maturity.
11. To be ahead of the curve of competitor and to provide
customers with effective way to make effective and
insightful decisions. Providing a place where data will
assist customers a place to tell their story in an
effective way.
12. System that provides a non-technical person with the
ability to diagnose a problem and indicates what to do