Define and describe the concept of Data Explosion
Companies recognize the value of acquiring and storing data for analysis and other purposes.
Consequently, companies have put significant effort into storing huge amounts of data. This
large-scale generation and storage of data for various purposes is called data explosion (Frew,
2021). Most companies have more data than they need and the rate of data generation and
storage is increasing.
There are many problems associated with data explosion that we need to address. The first issue
is data generation and storage require significant investment, hence it should be optimized.
Excess data will only result in money being wasted without any benefits. Furthermore, data
generation and storage require a large amount of energy. With the shortage of energy and
increasing cost of energy, companies can't afford to invest in unnecessary data.
Additionally, most data has a lifespan after which it will become obsolete and irrelevant. It is
reported that only 1% of data is being analyzed, while 100% of the data is costing money to the
companies (Frew, 2021). Hence, the question will be how to maintain optimal storage of data
that is relevant and used for analytics.
Discuss Thomas Davenport’s assertion that analytics are source of sustainable competitive
advantage.
As discussed above, companies are gathering a large amount of data on their customers, market,
trend, and their competitors. However, the quality of the data and the analytics used will decide
if the data will into insights or just excess data. Quality data supported with analytics can help
companies to increase cost-saving, enhance efficiency, achieve the market strategy, gain a
competitive edge and provide a personalized customer experience.
The article by Davenport (2020) elaborated on the pitfalls companies face in utilizing data and
how analytics play a significant role. This concept is further supported by Marr (2021), who
states that competitive advantage for companies will depend more on how well they can
capitalize on their data by applying analytics and other new technologies. Companies should
focus on the most important aspect that is revenue growth. When a company applies analytics to
large data they should focus on core business activities that are geared towards revenue growth.
Companies recognize the value of acquiring and storing data for analysis and other purposes.
Consequently, companies have put significant effort into storing huge amounts of data. This
large-scale generation and storage of data for various purposes is called data explosion (Frew,
2021). Most companies have more data than they need and the rate of data generation and
storage is increasing.
There are many problems associated with data explosion that we need to address. The first issue
is data generation and storage require significant investment, hence it should be optimized.
Excess data will only result in money being wasted without any benefits. Furthermore, data
generation and storage require a large amount of energy. With the shortage of energy and
increasing cost of energy, companies can't afford to invest in unnecessary data.
Additionally, most data has a lifespan after which it will become obsolete and irrelevant. It is
reported that only 1% of data is being analyzed, while 100% of the data is costing money to the
companies (Frew, 2021). Hence, the question will be how to maintain optimal storage of data
that is relevant and used for analytics.
Discuss Thomas Davenport’s assertion that analytics are source of sustainable competitive
advantage.
As discussed above, companies are gathering a large amount of data on their customers, market,
trend, and their competitors. However, the quality of the data and the analytics used will decide
if the data will into insights or just excess data. Quality data supported with analytics can help
companies to increase cost-saving, enhance efficiency, achieve the market strategy, gain a
competitive edge and provide a personalized customer experience.
The article by Davenport (2020) elaborated on the pitfalls companies face in utilizing data and
how analytics play a significant role. This concept is further supported by Marr (2021), who
states that competitive advantage for companies will depend more on how well they can
capitalize on their data by applying analytics and other new technologies. Companies should
focus on the most important aspect that is revenue growth. When a company applies analytics to
large data they should focus on core business activities that are geared towards revenue growth.
