Category: Data Analysis

Data is a material gathering and storage agent, to evaluate the activities of the present and the future of an event or situation. Data is used by organizations or individuals, to comprehend and observe the activities of consumers. It is about gathering facts according to certain needs or characteristics by the individual or organization carrying out the information gathering. Data is also used for business information collection to evaluate the employee growth in the organization and the perception of the consumer to the organization’s product or service.

According to import.io (2018) data is information a computer understands, but is humanly possible to understand. The computer decodes the data to a readable form that then makes humans understand to make predictive decisions. Data has always been the main source of information for organizations and individuals, it has helped organizations gain strategic positioning in the market, it gives organizations a bird’s eye view of their target audience, it helps organizations know the market share they own, and the market share they do not have. When an organization gathers data, this data is stored for future preferences, and to compare the difference in performance in the present and in the past.

Data gathering helps organizations make informed decisions that are business driven. Organizations that are information-driven use it for the main purpose of driving the audience to their business. It’s so important for such organization to use data because in data they can account for profits and losses. According to Marr (2018), organizations like Amazon compile data on their customers when they visit Amazon’s website to shop for products, Amazon monitors every product the customer clicks on and finds similar customers who have searched for such products to recommend more products that fir the customer. Data has helped Amazon become better at selling to their consumer, and it has helped them subtly market and advertise to prospective customers.

Data is extracted from every marketing and advertising activity an organization engages, marketing and advertising need data for future projection and targeting, one cannot simply exist without the other. Marketing and advertising and data have a symbiotic relationship, Marketing gives the need for data, as it has always been about the target audience, and in talking about the target audience, advertising will always be about who are they? Where are they? How many of them are there? How many of them can we reach and through which channel can we reach them. According to Worlu, Kehinde and Adegbuyi (2007) Advertising is about the product and the target audience, Advertising simply makes the product acceptable fit to the needs of the audience and converts the audience to become a buyer.

Bolajoko (2015) defines Advertising as a message to an audience that can be regional, local, or national by soliciting them to patronize the advertised service or product. Ourasang and Parande (2016) State that marketers and advertisers use certain criteria have to monitor the activities of their customers to measure the engagement of customers to the company’s business and sees big data as information gathering that brings proofs.

According to Matz and Netzer (2017), online customers shop for goods and they drop their personal addresses and preferences, and the information that a customer drop helps organizations to keep track of the needs of these customers in other to find other ways to satisfy these customers, and understand the way they think and predict future orders of the customer.

According to Rechia (2017) editorial, data sheds light to the buying behavior of customers that are diverse, near and far and this helps the company to have a more direct way of meeting the needs of these diverse customers, more importantly, marketing executives claim that 70% of data-driven marketing is the main driver of their campaigns.

The importance of Data

According to Competition and Market Authority (2015), the following are important benefits of data to firms.

1. It helps organizations grow sales through advertising that targets the audience
2. It helps organizations have statistics of their customers
3. It helps organizations and businesses recommend products that are customer specific
4. It helps organizations to improve and develop better products as regards customer needs.
5. It helps organizations to get better at understanding of customers and satisfy customer shopping needs.
6. Data help businesses understand when to give free gift.

According to Gruntdvig (2010) Data collection involves the following:

• a) Qualitative research method
• b) Quantitative research method

a. Qualitative research: is concerned with the world and how things are in the world, it is carried out in form of questions, it means the researcher ask questions on behalf of a certain situation and the subject answers the question based on his own knowledge of the situation. In the case of marketing and advertising, the firm can go to the audience to ask questions based on a product to understand the customer’s perception and also the organization will know a point of improvement if need be.

b. Quantitative research: is usually about sharing questionnaires to a small group of people to allow them answer questions on behalf of the firm mainly for data gathering purpose and information gathered is interpreted and then the interpretation is understood for the firm to take necessary steps towards the improvement of the product or the organization. The following are the use of qualitative research:

• it helps to discover new frontiers
• to look into attitude of the consumer
• it helps the firm under consumer habits
• it helps in study of a new product, and acceptance and the unacceptance of a product.
• it helps the firm know if their packaging is good or bad

Quantitative and qualitative research has been the method that marketing and advertising data are personally extracted from consumers, it has always been the way the firms collected data to analyse it for them to gather data.

However, marketing and advertising results today are easily gathered by the data being generated by the consumers who shop online through their shopping habit or who have registered to be profile users. An example is a Buzzsumo analysis in 2017, which was on the type of Facebook engagement that attracted the attention of users; it was revealed that videos got people more engaged.

The data above shows how brands carry out market research on businesses online, and it’s more of the perception of users on these brands and what form of marketing tools these brands employ to maintain customer relationships and build customer engagement. Organizations that are able to monitor customer engagement across platforms are accessing a data called big data.

As much as this information in data form are available, there are lots of organizations who do not have a more knowledgeable view of how to implement this data in business decision-making, According to a study carried out in 2015 by PWC and iron mountain on European and North American companies, it was discovered that organizations that find the information useful for competitive advantage is 4%, while 36% lack the skillset to apply the information acquired, this means that 43% of these organizations find no useful benefit and 23% cannot find an applicable use of the information. A similar study was carried out by PWC on Nigerian organizations, they have accepted and understood the use of data to an extent, but instead of using data most executives use human intuition even with crucial business decisions, it was discovered that 30% of Nigerian executives believe their organization to be data reliant, but scarce resources, budget affordability, implementation plan not fit for organization structure, government policies, market constraints cause drawbacks for them. Therefore, having data in the 21st century is one thing but implementing the information gathered is a different challenge entirely.

According to Redpoint (2016), the following are the changes experienced by data-driven organizations as it affects their execution and strategy.

The above diagram indicates that customer data gives rise to operational execution and the creation of customer strategy. The availability of data is sufficient enough for decision-making, but a misinterpretation of customer data cannot lead to good customer strategy and that would also lead to a misinformed method of executing operationally.

Data is about the customer and how information from customer preferences leads an organization to make better strategic decisions in branding, customer engagement, segmenting customers according to preferred needs, better modeling for key management conversations and customer-centric decisions.

All information from customer strategy shapes the operational activities in terms of executing informed advertisement and attractive promotion with customer-centric content to fit planned budget in the organization. To buttress this point Tesfaye (2017) states that data-driven organizations are equipped with future-oriented information that empowers them to satisfy both existing and new customers with better adverts.

References

1. Impot.io (2018, June 18). What is data and why is it important [Blog post]. Retrieved from https://www.import.io/post/what-is-data-and-why-is-it-important/.
2. Marr, B. (2018). How amazon uses data in practice [Blog post]. Retrieved from https://www.bernardmarr.com/default.asp?contentID=712.
3. Worlu, .R., Kehinde, .J., and Adegbuyi, .O. (2007). Marketing: principles and applications, Port Harcourt, Nigeria.
4. Bolajoko, N.O. (2015). The fundamentals of business policy and strategy, Shomolu. Lagos
5. Ouarasang .G. & Parande, .V.P. (2016) Big data analytics framework for advertising and marketing. 3(8), 18-39.
6. Matz, .S.C. & Oded, .N.(2017). Using big data as a window into consumer psychology. Current Opinion in Behavioral Sciences, 18, 7-12.
7. Rechia, .C. (2018, March 26). The ABC’s of data-driven marketing [Blog post]. Retrieved from https://www.forbes.com/sites/forbesagencycouncil/2018/03/26/the-abcs-of-data-driven-advertising/#3af1ca8416b2.
8. Competition and marketing authority (2015). The commercial use of consumer data: Report of CMA’s call for information. London. Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/435817/The_commercial_use_of_consumer_data.pdf
9. Gruntdvig (2010). The methodology of qualitative research for consumers organizations. Education and Culture Lifelong Learning Programme. Retrieved from https://www.coursehero.com/file/24000919/Handbook-Methodology-of-qualitative-researchpdf/
10. Buzzsumo (2017). Facebook engagement by content format type [Blog post]. Retrieved from https://heidicohen.com/facebook-engagement-declines-research/
11. Iron mountain (2015). First-ever information value index from PWC and Iron mountain [Blog post]. Retrieved from http://www.ironmountain.com/resources/infographics-and-tools/f/first-ever-information-value-index.
12. PWC (2016). PWC’S data and analysis survey 2016. Retrieved fromhttps://www.pwc.com/ng/en/assets/pdf/bd-infographics.pdf.
13. Redpoint (2016). Mind the gap. Retrieved from https://www.redpointglobal.com/wp-content/uploads/2016/12/RedPoint-Mind-the-Gap-Ebook-EB-MINDUS0916-02-WEB.pdf
14. Tesfaye, .B. (2017). What is the influence of big data and analytics on Management control systems? Nijmegen School of Management, Radboud university. Retrieved from https://theses.ubn.ru.nl/bitstream/handle/123456789/4917/Tesfaye%2C_Behayilu_1.pdf?sequence=1

In today’s data-driven world, organizations across various industries heavily rely on accurate and reliable data to make informed decisions. However, ensuring data quality can be a complex process, involving numerous factors and considerations. The American Health Information Management Association (AHIMA) has developed a comprehensive Data Quality Management Model to guide organizations in effectively managing and improving data quality. In this informative essay, we will explore the AHIMA Data Quality Management Model, its key components, and its significance in enhancing data integrity and decision making.

The AHIMA Data Quality Management Model provides a structured framework for organizations to assess, improve, and maintain data quality throughout its lifecycle. It consists of six key components that encompass various aspects of data quality management: data governance, data architecture, data management, data quality assessment, data integration and interoperability, and data analytics.

Data governance is the foundation of the model, focusing on establishing policies, procedures, and roles to ensure accountability and responsibility for data quality. It involves defining data stewardship, data ownership, and data access policies, as well as establishing data governance committees and processes for data management.

The data architecture component involves designing and implementing a robust infrastructure that supports data quality. It includes data models, data dictionaries, and data standards that ensure consistency and uniformity across the organization’s data sources. A well-designed data architecture facilitates data integration, accessibility, and data security.

Data management focuses on the processes and procedures for collecting, storing, and maintaining data. It encompasses data collection methods, data entry and validation processes, data storage and backup strategies, and data retention policies. Effective data management practices ensure the accuracy, completeness, and timeliness of data.

Data quality assessment involves evaluating data against predefined quality standards. It includes data profiling, data cleansing, and data validation techniques to identify and rectify data errors, inconsistencies, and anomalies. Regular data quality assessments help identify areas for improvement and ensure that data meets the organization’s quality requirements.

Data integration and interoperability address the ability to share and exchange data seamlessly across different systems and platforms. It involves establishing data interfaces, data mapping, and data exchange protocols to enable data interoperability and integration. Effective data integration ensures that data from various sources can be merged and analyzed to derive meaningful insights.

Finally, data analytics leverages advanced analytical techniques to derive actionable insights from data. It involves data mining, data visualization, and predictive analytics to identify patterns, trends, and correlations. Data analytics enables organizations to make informed decisions, improve operational efficiency, and gain a competitive advantage.

The AHIMA Data Quality Management Model is significant for organizations as it provides a systematic approach to managing data quality. By implementing the model, organizations can achieve several benefits. Firstly, it enhances data integrity by ensuring that data is accurate, reliable, and consistent. This, in turn, improves the credibility and trustworthiness of the organization’s data assets.

Secondly, the model supports evidence-based decision making. Reliable and high-quality data serves as a foundation for informed decision making, enabling organizations to identify opportunities, mitigate risks, and drive positive outcomes. By adhering to the AHIMA Data Quality Management Model, organizations can confidently rely on their data to make strategic and operational decisions.

Furthermore, the model promotes data-driven innovation and research. By maintaining high-quality data, organizations can conduct meaningful analysis, identify insights, and drive innovation in their respective fields. High-quality data also supports research initiatives, enabling researchers to draw accurate conclusions and contribute to advancements in various domains.

In conclusion, the AHIMA Data Quality Management Model provides a comprehensive framework for organizations to manage and improve data quality effectively. By implementing the model’s key components, organizations can enhance data integrity, support evidence-based decision making, and drive innovation. Embracing the principles of the AHIMA Data Quality Management Model empowers organizations to leverage the full potential of their data assets and thrive in a data-centric world.