As the business world delves deeper into the
possibilities of technology, there is no doubting the power and importance of
data. When it comes to increasing your sales profitability, it can be difficult
to know which steps are best to ensure that your people are making the most of
the avalanche of data now available to them. Businesses must manage data from a
myriad of sources – and it is important to know which channels are worth your
time. With a landscape that is growing more complicated every day to increase
efficiencies, manage costs while continuing to show profitability, there is one
vital resource which businesses are not taking full advantage of.
This resource is offline data. The power of
offline data is that it allows you to see behind the scenes of what is really
driving your decisions, as well as giving you the confidence to know your data
is whole before then using it to develop important strategies.
Did you know that large amounts of customer
activity happen offline, including 90% of retail sales? This means that sales
and marketers are missing huge amounts of data, data which could make or break growth
strategies and decisions.
Data onboarding allows you to combine your
offline data with your online data, allowing you to use both types of data with
a complete understanding of what’s going on. In doing this, you are able to
create targeted, personalised communication for customers, and in a time where
customers need individual validation this is a smart decision. Data onboarding
take the offline data and then links to the relevant data in the digital world,
resulting in far more relevant strategy.
Having incomplete data is a real concern for
businesses and markets alike, and it’s easy to become overwhelmed. At AtoBI, we
are able to ensure you are making the most of all of your data, offline and
online. You can be confident that our team will give you all the information
you need to make the right decisions for your business, putting you one step
ahead. For more information, visit our website or contact us at info@atobi.com.au.
Artificial Intelligence is now
everywhere. From a Science-Fiction concept to an widespread technology, it
often comes with clichés and misconceptions (Yup, we’ve all seen how it ended
in 2001 Space Odyssey, The Matrix or Terminator…)
More and more organisations of all
sizes and industries are trying to understand how they can leverage AI and get
aboard the hype train. But from a buzzword to an actual solution delivering
strong value, their are a few things to keep in mind before taking the leap.
Artificial Intelligence projects can
fail if they are not led for a really, really good reason. This might seem
obvious, but having witnessed the significant impact they have on an
organisation, the business case must be ambitious though pragmatic, focusing on
tangible business outcomes. Also, and as sexy as AI can be, is it the most
adapted technology to solve that specific business case?
Until future systems can embrace human-like top-down
decision-making,
current Artificial Intelligence technologies relies on data. A lot of data. It
must combine quantity with quality, needs to be clean, comprehensive and avoid
siloed information, biais or partial data (limited history, demographics…)
If it is sometimes challenging to
figure out what data you will need for your algorithms to learn and deliver
efficiently, working backwards from the problem you are looking to solve can
help determine where to start.
In today’s digitized economy, the
ability to use data represents a real and essential competitive advantage. To
get to a future state of mature analytical competency, there’s real work to be
done in integrating the data you have already. This is a strategic goal for the
entire company and, when addressed properly, will lead you to develop
experience and a data infrastructure that unlocks every next step. HBR
In other words, walk before you run
and get a good understanding of your data first: Conduct an audit of what data
you have and where it sits, making sure you have easy access to all relevant
information.
As mentioned earlier, AI, Machine
Learning (ML), Robotic Process Automation (RPA) are subject to many
misconceptions. Amongst the most common ones are:
AI is a physical entity – Yes, some people still think
of AI as a Terminator-like robot. Well, HAL 9000 would almost seem like a
more accurate description…
AI can think like a human-being – Replicating the complexity
and flexibility of a human brain is way beyond existing AI technologies.
“The biggest misconception around AI is that people think we’re close to
it.” Prof. Gary Marcus, NYU
AI can learn on their own – You have to teach them how
to learn and improve, tweaking their algorithms constantly and feeding
them with data
AI will take your job – It doesn’t replace jobs, it
makes them more strategic, according to many serious studies
AI can use and figure out your
messy data – As
seen above, you need to figure it out first.
Education is key, not only to raise
awareness around what AI will bring to the business at an operational level,
but also to get executives onboard, promote data literacy and manage their
expectations. Support from the top-management is critical to drive change, get
access to all data, overcome political hurdles and lead with vision.
Driving AI projects is everything
but a one-off process. The best performing organisations have in-house experts
and developers constantly improving models and algorithms, making sure they are
updated on a frequent basis and adapting to the latest requirements and
information.
Project Management methodologies
also need to be adapted to this iterative approach, ensuring constant alignment
with Business Outcomes and continuous improvement: Scrum, Agile…
We have seen how strategic your data
is, especially when it comes to AI. Data governance gives you a real
competitive advantage, as it will directly impact the quality of your
predictions and algorithms. An efficient data governance strategy must cover:
Security – Managing access to data
Integrity – Making sure the data is
accurate
Loss prevention & backup – Mitigating data
loss/corruption risks and protecting privacy, especially when it comes to
sensitive information
Lineage – Keeping full transparency
for everyone to understand where the information comes from and how it’s
been processed, avoiding the “black box” effect
Completeness – Avoiding biais and partial
views
Ethics – This is sometimes overlooked
but we cannot emphasize enough how critical this is, in business as in our everyday life.
Sales reps face the constant battle of time
management, and when it comes to completing those make-or-break tasks, finding
the time can be difficult. By implementing a sales automation process, your
business can maximise efficiency. In both a professional and personal sense, AI-powered
software and tools are becoming a normal way to save time. Not only do
companies with a sales automation process have a 53% conversion rate, they also
have a 3.1% increase in annual revenue. Automating sales tasks is a budget
friendly way to reduce your operating costs by up to 90%, according to a recent
study.
There are a few sales tasks in particular
which can benefit from automation, such as reporting, email personalisation and
pipeline management.
When it comes to reporting, by automating your
reporting process you free up valuable time to spend improving your business in
other areas. As a sales manager, it can be frustrating when you’re scouring
through out-of-date information to pull together a report. By automating this
process, you can have confidence that your data is always up to date and
relevant.
By personalising your email content, your
audience will feel that you understand them, increasing your engagement rates.
Personalising the first and last 10% of your emails seems to be the most
effective strategy, and you can gather this data using a customers purchasing
history, their personal information like age and gender, as well as tracking
their awareness of your product.
The best way to monitor your leads is by
automating your pipeline management system, which removes the urgency of manual
tracking and ensures that quality leads aren’t lost. Automated sales processes
allow you to generate leads with more accuracy, increasing your retention rate.
Your lead prioritization and distribution can also be improved with automation.
Sales automation helps your employees function at
their best. A good sales automation system is easy to use, functional and effective.
At AtoBI, our team can help you achieve the highest level of efficiency using
data. Contact
us today to find out more about what setting up a sales automation
process can do for you.
Technology has transformed the way businesses function, and this
all boils down to one thing: data. Data has opened the door for businesses to
analyse their inner-workings, and make more informed, confident decisions
backed up statistics. However, when it comes to the way businesses gather and
process their data, there are a variety of revenues for this information and
one destination – your company’s systems. So how exactly is this data compiled
and broken down? This is where data modelling comes into the equation.
Data models are a necessary part of defining the data a business
receives, giving organisations a centre of information to build around. Data
models determine how data is represented and used, and it plays an important
part in implementing changes to a company’s operations. It is important to
trust your data model, and to plan out how you want to gather and store your
data.
Different Types of Data Models
There are a number of data model types, each catering to a
different business purpose. Each data model has a different processing
procedure, which helps businesses comprehend their data and find ways to
utilise it.
Star Schema
Star Schema is named after it’s star-shaped diagram, and its
purpose is to question huge informational indexes. Star Schema is often used to
help business insight, OLAP cubes, analytic applications and impromptu
inquires. While it has one of the least complicated architecture, a star schema
is one of the more popular data models.
Physical Data Model
A physical data model functions by highlighting the structure of
data. This refers to things such as the communication between computer
procedures or tables and columns. The formation of the data model changes with
the specific needs of the procedures, and works with the information to
guarantee successful execution.
Logical Data Model
A logical data model takes the framework of the data components,
and then sorts these components into a system, noting the relationship between
them. This data model can be the foundation of the physical data model, and
also filters the data components which a conceptual data model presents.
Enterprise Data Model
The enterprise data model gives an overview on all data a company may have. This data model gives a broad, yet specific diagram of the organisations data, and helps in creating other database parts such as entity relationship diagrams and XML schemas.
Wanting to make the most of your data, but unsure where to start? At AtoBI, we are experts in data analytics, and our professional consultants are ready to help your business thrive. Contact us today to find out more.
Advancements in technology have seen the world
of business embrace data, making it easy to assume that everyone is taking
equal advantage of this new resource. Recent studies, however, have revealed
that companies across a range of industries are struggling to establish a data
driven culture. Randy Bean and Thomas Davenport report that
72% of survey participants have yet to forge a data culture, and 53% state that
they are not yet treating data as a business asset.
There is endless value in data, and by
creating a data-centric culture that understands and utilizes this resource you
are better able to manage the associated risks, while reaping the many rewards.
When it comes to creating a data driven
culture, there are a few things you can do. The first is to invest in the right
data related equipment and tools. Whether this is a machine learning tool to
provide data quality assurance checks, or business intelligence software to
help create better end-user processes. Once you have the right tool set, an
effective way to engage your staff is to open your tools to wider data pools,
engaging more areas of the business and multiple branches of staff. This
creates a culture of data enthusiasts, opening up a space where data is not
only accepted but sort after.
Educating your staff will go a long way in
helping them understand and embrace data. Every API is different, so ensuring
your staff are aware of the relevant nuances allows them to better strategize
using the data they have acquired. If your CIO is able to set data-centric
goals which bring value to your business, then data becomes an actionable tool
to improve your business in a multitude of ways, and with KPIs in place your
staff will be more motivated, more educated and your business will thrive.
Are you confident in your ability to make the
most of your data? At AtoBI, we understand every aspect of data analytics, and
our professional consultants can help you create a data plan best suited to
your business needs. Contact us today to find out more about how
data can transform your business culture.
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