DATA ANALYTICS (Research Paper Sample)

Data Analytics
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Introduction
Data analytics dedicates meaningful information from large amounts of unstructured information gathered through research or routine commercial activities. The objective of data analytics inside a company is to build a plan based on findings drawn after evaluating the data obtained. The strategy should be to improve the company's market or goods in the market for the firm's benefit (Mikalef et al., 2020). Therefore, experts may keep tabs on different parts of the company and determine where adjustments are required and where things are going well with the help of data analytics.
Every computer equipment nowadays generates data in real-time as a user utilizes it; this is the nature of the modern digital world. Similarly, companies generate data as they go about their daily operations; specialized algorithms may then evaluate these records to reveal previously unseen patterns and sequences that can be exploited for profit (Ardito et al., 2018). Even while data analytics have a relatively modest number of potential uses for a firm of this size, the possibilities for expanding the company's customer base and product offerings that emerge from the analysis of this data are vast. The organization needs sufficient processing capacity to analyze the entire lump of data and a safe, secure location to store all the data it generates.
This report will include the following topics: business problem setting; data analytics principles; product design, development, and assessment; and a thoughtful review of the consequences of inquiring about the learning growth in this program. Data analytics is where I will demonstrate how the most important algorithms and models are put to use in creating actionable insights for businesses. Data transmission, processing, and analytics are also included from the standpoint of an enterprise system, as are the characteristics of various data storage options. The many options for platforms to create and implement data storage, processing, and analytics solutions in various data situations are covered.
Business challenge context
The marketing challenge
The department's data is analyzed to determine the most important KPIs for marketing impact. Through investigation of the market, they find this information. Primary and secondary sources, gathered from either internal data or external participants, are distinguished in this study (Hamilton & Sodeman, 2020). Using surveys and questionnaires to collect primary data is expected in research. Secondary data, on the other hand, comes from things like past marketing research or actual sales figures. As a result, it is necessary to glean secondary data from related papers.
Various tools are required to retrieve this information from the big data warehouse. Important information can be extracted from large data sets using statistical and machine learning data mining algorithms. Google Analytics is one example of a tool that fits this description.
Google Analytics
Powered by Google, this is a service for mining data. Dependent on the plan selected by the customer provides valuable information for the company (Dong & Yang, 2020). It is a powerful website that can be used to monitor the flow of visitors to the company's website, allowing for an assessment of the market's strength in both established and emerging regions.
Suggestions and insight
Monitoring Expenditure and marketing costs
With data analytics, the company may shift its resources away from less lucrative regions with steady client bases and into more lucrative ones with weakening competition. It is impossible to do this without obtaining the data through some analysis (Mohammadpoor & Torabi, 2020). The corporation will need to establish a plan to ensure that this choice bears fruit. Therefore, the plan will highlight places where little can be done, but big gains are anticipated.
Market research
In order to better understand the market's current status, data analytics may be employed to conduct such analyses. Data research on the target market, for instance, may assist in determining the market's worth by calculating the potential return on investment if the company grows into that sector (ROI).
Data analytics Principles
Competitor analysis
A company's success in a given market is heavily influenced by the strength of other enterprises operating there. If a company's main rival is formidable in a particular market, it will likely affect its sales and client base. For the converse situation, the same stipulation is factual (Liang & Liu, 2018). Suppose a company wants to improve its product or service offerings relative to its rivals. In that case, it must deliver them in a new, more appealing package or presentation.
Analyzing the market is a great way to get information about the companies operating inside it so you can better understand your competition. Researching or gathering intelligence about the competition can help you do this. Research of this nature seeks to reveal the rival firm's market expansion strategy and techniques. Since part of this data may be the private intellectual property of the genuine firm, it is often gathered from the public domain (Upadhyay & Kumar, 2020). Businesses' annual reports and other records kept by the company, government publications, and stock market reports are all examples of secondary sources of information.
Numerous freely available resources aid a company in gathering information about its competitors. There is a fast search feature in the analytical tools. This social media search engine aims to give users an overall picture of a brand's performance in the marketplace. The software can track any company mentions on the service, whether made by influencers or regular users. Additionally, the software may be used to learn about the age range, sexual orientation, geographic concentration, and temporal patterns of the company's employees (Akter et al., 2020). Therefore, with such a device, the company may learn about the extent of the competition's market penetration while gaining access to valuable data. Other analytical approaches for competitor research can detect technologies employed by the rival firm as they gain or lose market share and the rate at which they adopt such technologies. Some of these tools need payment to describe their analysis, while others are open-source and free to use thoroughly. Examples of such tools are WooRank, BuiltWith, and the Google Adwords Key Planner.
Benefits for analysis
The firm might gain a lot by employing analytical methods and concepts. They may, for instance, learn about the market's shifting fashions and figure out when to enter at the optimal moment for maximum return on investment (ROI), among other benefits. Additionally, the tools can provide possible market forecasts, which is crucial when planning to control and utilize the market to a company's advantage.
Suggestions and insight
* Strategize and implementation
The data may be helpful in specific ways, but more is needed to save a company from the intense competition it faces from others operating in the same sector. Data generated by these analytical tools through data mining must, of course, be assessed. With this method, businesses may cherry-pick the information the tool collects based on its relevance to their needs. Decisions based on such information necessitate a practical plan for carrying them out.
* Learning curve
Instead, such data utilization might shed light on the competitor's business strategy and how it has been implemented. Sometimes such data is not immediately usable, but it can still be used to make crucial judgments. Therefore, the user may utilize the same data to uncover potential business prospects by observing the connections that the rival firm has and depends on.
For each word in a line of text from one or more input documents, the Mapper generates a pair consisting of the document ID and the number of occurrences of that word (Listing 1).  NLP techniques, such as de-punctuation, lemmatization, and stems, are applied to each word. Using specific values and keys is required for lighter handling of Objects' serialization by developers. Text and Int-Writable are examples; Hadoop prefers them to String and Integer because they provide a more superficial overlay on top of byte matrices while storing the same information.
def mapper(document_id, text):
# Apply NLP techniques
processed_text = preprocess_text(text)
# Split text into words
words = processed_text.split()
# Count occurrences of each word
word_counts = {}
for word in words:
if word in word_counts:
word_counts[word] += 1
else:
word_counts[word] = 1
# Generate (document_id, word_count) pairs for each word
for word, count in word_counts.items():
yield (document_id, (word, count))
Listing 1: Inverted Index Mapper
After the words have been mapped, the intermediate data created by the mappers are aggregated using a combine function and then sent on to the reducers. As illustrated in Listing 2, the combiner totals up each word's occurrences in the text and then outputs a pair consisting of the totals.
def combiner(word, counts):
# Total up the counts for each word
total_count = 0
for count in counts:
total_count += count
# Output a pair consis...