Introduction

 

My question for
the dissertation is: Why small businesses are not using data mining?

This question I
chose mostly because I am working really close with college data. This subject
is something that I really like to do. I am interesting in news, books and
articles about data. I really proud when I achieve something new, that before I
did not know.

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Data mining in
business is not a new phenomenon, but data mining tools are often used by large
companies. Managers of large companies value the importance of data analysis
and can allocate more money. Small businesses are often faced with the problem,
since most data mining tools cost a lot compared to company revenue, so small
businesses use elementary spreadsheets, such as MS Excel, to analyse data if
needed. However, the result is often not as beneficial as the use of data
mining tools. Its topicality is the fact that more and more small business
executives seek to optimize their companies’ activities, to effectively use the
accumulated data, to save money and to clearly assess the company’s prospects
for planning further activities.

In this task I
will analyse literature. I will write more about what I read it already.  I will critically analyse the other people
research or opinion. I will justify this literature. This task will be
beginning of the dissertation. I am sure that this literature review will be
bigger and bigger every day. My aims and objectives will be included in this
review as well, to show my visions for the project.

 

Aims and Objectives

 

Aims of the
project:

·      
To
get clear answer why small businesses not using data mining.

·      
To
find out what is the biggest problem of not use data mining.

·      
What
advantages and disadvantages can be of data mining in business?

·      
How
much people know about data mining?

·      
What
is people understanding of data mining?

Objectives of the
project:

·      
Questionnaires
and Interviews

·      
Good
analysis of questionnaires and reviews

·      
Deep
research on different sources: webpages, books, magazines and news

·      
Look
for professional advice or opinion

·      
Visit
different relative events or seminars, if there is any.

·      
If
needed do discussions group to get better views of different people thinking.

All this I have
set for myself to achieve best results for my dissertation writing. This list
will get bigger later on, because after reading new sources might I will get
new ideas where to look for good answers on my topic.

 

Relative source overview and analysis on my chosen topic

 

Nick Ismail in
‘Information age’ webpage describes data mining as very important subject in
these days.

According Nick
“it’s good to know that data mining can actually improve retail different
services. Namely, it is a well-known fact that retail businesses are relying on
web scraping.” (Ismail, 2017) As Nick saying in
this article data mining is like selling expensive stone. Before companies
could not let themselves to proceed data every day or every second as it is
today. Big organisations which has been working 24/7, could not update data. So
many of the organisations had irrelevant and old data in the systems, because
to update data, databases had to be switched off. (Ismail, 2017)

Pui Mun Lee from
Singapore University said, that “Some sources use the terms data mining,
business analytics and business intelligence in an interchangeable manner.
However, in strict theoretical sense, there are distinct differences among the
three terms.” (Lee, 2013) He explained that
all the terms in simple form, all of them are the tools in between, to
processed data. If organisations using these tools mostly they will get better
results out of it. Together they are all powerful, but not in all cases.

 

 

 

 

 

 

Figure 1: 4
stages of data mining in business ( (Inc, 2010))

 

These four stages (figure1) have been
introduced in Datamining Tools Inc slideshow. The hardest stage according Pui
Mun Lee is preparation, because prepare data it could take a while. In
preparation stage could be included data cleaning, data sorting, and data conversation
to specific format that is suitable for data mining. (Lee, 2013)
According Pui “data transformations (converting data values through
normalization, mapping, and/or aggregation), and data reduction (combining data
that involves large number of variables into a smaller set of variables).” (Lee, 2013) So all this and
probably more is just a first stage out of four.

I am writing about this, to show that data
mining could be hard process and small business would not have enough staff or
money to do that. So they are chooses more real life data mining, like giving
people posters and expecting them to come back to the shop or just do simple
marketing research.

According BFS Capital “while it’s not
realistic for many, if not most, small business owners to mine data on a large
scale, every small business owner should know that basic data analysis can be
done relatively inexpensively through open source or free tools.” (Capital, 2016) BFS Capital states
that in 2016 there were many small businesses that did not use data mining,
because of lack knowledge about tools, prices and what is data mining. The
mixture between marketing research and data mining was in between business
owners. All this article is about to tell small businesses owners that data
mining could make better understanding of customers, it could make business
work for them but not upside down. Out there, in the world, are plenty of
software and online tools to start using data mining.

According Data Mining Techniques: For
Marketing, Sales, and Customer Relationship Management, Edition 3 book “Who can
be a data miner? The answers is not everyone, some specific skills are needed.”
(Berry, 2011)
In this book clearly said that people without good skills cannot be the data
miner. Data mining processes are complex, but understandable, so any of us do
not need be a doctor nor have high level in analytics. This one is another
reason why small companies are not using data mining, lack of skills or lack of
money to hire new people to do data mining for them.

Again in these days are plenty of cheap tools
for data mining, one of the good examples are Google Analytics cheap, no need
to do much Google does everything for you. (Westfall, 2017by )

According ePrints article „A study by the European Union
(EU) funded BLUE-ETS project found that “small businesses use data modestly”
and “the main obstacles preventing businesses from using NSI statistics (more
intensively) include lack of interest” (S, 2016) In the same article
Mr. S.Coleman said „Many large
organizations have moved into data analytics by partnering with an IT company
that can provide these capabilities”. (S, 2016)
This is very true many of large organisations looking for opportunities to find
good contractors in IT that can do data mining for them. In some cases large
organisation opening new departments of IT or data mining department. Many
small companies cannot afford to do that. Or owners of these companies have no
clue what data mining is for. They have very little knowledge of their
customers and their needs. From my own experience I know couple companies in
construction, these companies are very small, no more than 10 people working in
there. Owner of the company sits all day next to phone and waits for new jobs.
He has no idea how data mining could help him to improve what customers’ needs
are, might he should buy new technique for new jobs, or might he just need
online booking services for new jobs.

 

One better example why any business should use
data mining companies or qualified people is “Another example is Spotify. The
company offers users an ad-free music streaming service on a subscription
basis. Their main business is music; however, they use datamining techniques to
link songs together and they bought Echo Nest, a company involved in music
analytics, to help them do this. One of the main reasons for the company having
a turnover of £131.4 million in 2013, a 42% increase from the previous year,
was because of their effective utilization of data analysis .In 2013, 4.5
billion hours of music were listened to; Spotify was able to offer a better
experience to users, with recommendations of similar music in addition to the
music they are currently listening to Spotify can also predict what songs are
likely to make top of the charts; Spotify could not have achieved this success
if they had worked alone.” (S, 2016) This examples
clearly explains very well how big company increase income, very quick. So
smaller companies or organisations should try at least look for information how
to make business earn more money. In this way all these companies could became
industries. Just need to look for new technologies to begin.

Advantages and Disadvantages

 

According Zentut
webpage article disadvantages of data mining are “Privacy issues, security
issues and misuse of information/inaccurate information.” (Zentut, 2017) Privacy problems started when all
social media came to the world, and become very popular. E-commerce become very
popular, so people are afraid sometimes that collected information about them
could be leaked or sold to other businesses. I completely understand people who
afraid of this when in newspaper or TV news, they are seeing incidents like: “Twitter
data leak: William Godfrey from Bethersden prosecuted” (Barlow, 2018 ),” Indian data leak looks to have been
an inside job” (Chirgwin, 2018 ) So people, or just call them small
business owners are afraid to trust software or tools where data can be
collected and processed, because there will be people who would kindly sell
information, without owner permissions.

 

Next disadvantage
is security, if businesses will decide to start using data mining, question how
to protect that data about customers, few members of staff, income,
expenditures and etc. To protect data, businesses will need to invest in data
security as well and that’s cost big money. Some newest articles about hacked
data “‘Professional’ hack on Norwegian health authority compromises data of
three million patients” (Shah, 2018)
and “Lebanese spies ‘left thousands of hacked text messages on open internet'” (Murphy, 2018) These are only
couple articles of thousands, which proves that hackers improving their skills
as well. And again this could be one of the reason why small shop in village,
will think that they do not need data mining, because pay money for tools or
hire new IT staff, invest in hardware and software for that IT guy and still be
at risk to lose everything. So this is the logic behind small business owner.

The third, but
not the last one disadvantage is about misuse of information or inaccurate
information. Sometimes, especially new staff of member could use
data/information without any permission if they have access to the data and
this is what it calls misuse of information in data mining. There is no way to
find out when, where or who can leak data or sell data, or use for own
purposes. So this disadvantage is really close to second one security, only
qualified people should get access to data which should be kept secure, and
data security should be one in the top list in data mining.

 

All these three disadvantages
are very close to each other, if business improving and investing in one ,then
it should be invested in the other areas. In same time this disadvantages could
push small businesses back for not using data mining, without good
investigation in advantages of data mining.

 

According
UniAssignmentCentre essay there is couple of most common advantages for data
mining “Fast and feasible decisions, powerful strategies, competitive advantage”.
Of course there is much more advantages for the different types of business,
and more simplistic but very powerful like: could help increase income or
better understanding of customer trends. (Essays, 2013)

Fast and feasible
decisions could be made by using data mining. Just imagine if one person, let’s
say owner of business sit in office and looking for useful information, this
probably will take a lot of time, so it would not call as fast decision. But if
he would use data mining tools, then all data could be processed and finished
in very quick time, with more accurate results, then owner will have time to do
better comparison of product or services or compare information with other
businesses.

 

When business
will start using data mining, then they will be able to do powerful strategies,
because when data will be processed, business can do analysis in different
dimensions to make various types of strategies and implement it. (Essays, 2013)

 

Overviews of Why Small businesses are not using data mining?

 

The issue of the
use of data mining tools for large enterprises is widely discussed and analysed.
It provides a wealth of software products for processing and analysing data.
However, it has begun to focus on small businesses that can adapt data mining
tools. The main reasons why data mining systems are not used in small
businesses:

• Lack of
information on data mining systems;

• The belief that
data mining tools are expensive and require high operating costs;

• Failure to
provide company benefits;

• The need for
professionals who are not able to recruit small businesses;

• Additional
expenses for employees.

It is also
commonly believed that the data mining tools are complicated and difficult to
understand. Most small businesses avoid using data mining systems because they
do not know the true benefits of data mining tools for the company. However,
not all companies do. Many years ago, Melissa Solomon explored the desire of
small businesses to use high technology. She highlighted ten IT areas where
small businesses were planning to invest money. Although security products and
communication programs were ranked first, data mining was included in the list.
In this case, it is presented as a tool for analysing client actions. By
gathering information about what they are and what they are interested in, it
can be used to analyse the work of the company and to take into account the
demand for planning new offers. (Solomon, 2005)

However, what
kind of data mining is important for small businesses is well disclosed by
Barny Ritholz in the article “Intro to Data Mining for Small
Businesses”. The basic idea behind the idea is that data mining can help
companies to respond to market changes earlier and help them survive even
financial crises. The paper states that if small businesses themselves use data
mining tools, they would soon be able to see market changes, their trends and
various anomalies. This would have responded more quickly to changes than when
the economic bureaus reported the market crises of collecting and processing
information from thousands of similar companies. In this case, this time
difference can be very significant and help the company avoid loss-making
investments. (Ritholz, 2009)

Small businesses
should focus on data collection and analysis. One of the main and most
important data mining functions for small businesses would be prediction.
Estimating enough indicators would make it much easier for companies to plan
their activities, future revenues and costs. Depending on the company’s
activities, it is possible to forecast its revenue, sales, number of orders,
etc.

The shopping
basket analysis can also be of great benefit to companies selling products. By
evaluating which sets of goods are most often chosen by buyers, it would be
possible to carry out various promotions, when buying one set of goods while
offering another, is usually bought together. Knowing consumer habits makes it
much easier to convince them to buy.

Data mining in
small businesses can be used in a variety of ways, but using data mining tools
in the right way, the company will always work more efficiently and
efficiently. Much depends on the company’s performance and the data mining
tools it chooses.

Today there is
plenty tools for small businesses do data mining.  According Sara Angeles “Big Data isn’t just
for big businesses with big budgets. Today, small business, too, can reap the
benefits of the massive amounts of online and offline information to make wise,
data-driven decisions to grow their businesses.” (Angeles,
2016)
She introduces 8 different tools for small companies:

·      
ClearStory
Data- this tool compares business data with public data, for better analysis.
It is easy to understand, because it has dashboard layout, where you can create
many different visual information tools, like graphs.

·      
Kissmetrics-
online application for marketing prices starts from $120 per month.

·      
InsightSquared-
tool for external data analysis. Price starts from $65 per month.

·      
Google
Analytics- tracking and analysing customer on your webpage.

·      
IBM’s
Watson Analytics- platform automated and simplified, that anyone can do data
mining.

·      
Canopy
Labs- platform for customer analysis, and sales, which gives very detailed
view about customer

·      
Tranzlogic-
it is tool for payment system, which could give you better view of marketing,
customer need, what promotion can be done.

·      
Qualtrics

(Angeles,
2016)

I have listed
applications or platforms which small businesses can use for data mining if
they will do better research of why they need to do, what advantages they could
get or where to find these tools, probably then all would have better income,
will understand their customers better. The main reason why they do not using
data mining already is bad knowledge what data mining is.

Conclusion

 

My
all assignment explains more about data mining. This is just an overview of why
business not using data mining. In my research I could not find straight answer
why they not using data mining which could give me better understanding, but
this makes me do bigger investigation with primary research, like surveys,
events, seminars etc.

In
my opinion this is enough to prove why I would like to investigate this
question in more details.

So
small businesses not using data mining, because lack of knowledge what is data
mining. Lack of understanding why they should use and how they could improve
their business.

There
is much more factors for not using data mining like costs, shortage of
qualified staff, security issues and staff training issue, this cost money as
well.

In
my opinion small business should start using data mining tools which I have
listed above for short period of time and if they could see benefits of data
mining, then they could think about more expensive and more professional ones.
Owners could start looking for the right person to do that job, or invest in
one of the staff to do trainings.

With
the advancement of modern information and communication technologies, the
amount of data processed and stored is rapidly increasing, therefore the task
of data analysis becomes more complex, it is difficult to make fast, efficient
and correct decisions. Data analysis is often used for data mining. Data mining
is applied in various areas: business and commerce, engineering,
telecommunications, banking and insurance systems, e-commerce, medicine, etc.
Data processing and knowledge retrieval are often used for data mining systems
that allow processing of various data volumes.

Data
mining, like statistics, is not just simulation and forecast, but an entire
problem-solving process. Understanding what business needs really is crucial
for successful data retrieval, since even today’s most sophisticated algorithms
cannot accurately and properly measure it. However, it should be emphasized
that data quality is also a very important aspect of data mining, since it is
only from high-quality data that it is possible to extract high-quality data
and perform qualitative data mining itself. In reality, fulfilling this
condition is quite difficult, because actual data usually not ready for data
extraction straight away. The fact that the computer can be used to find the
necessary data models or rules is the main idea of data extraction. But all
this is where small business can improve when buying more expensive and
professional software and hiring qualified people.

 

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