5 reasons to invest in Data Analysis
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and make the decision based upon the data analysis. Here are some 5 reasons to invest in data analysis below.
Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining, text analytics, business intelligence, combining data sets, and data visualization.
Why Invest In Data Analysis?
1. A Deeper Understanding Of Customers
There’s greater access to sophisticated analytics, such as AI, as well as third-party data that fully illuminates the digital footprints of online customers. Decision-makers can now study more factors, including affluence, different levels of price sensitivity, affinities to different brands, and key behavior traits of potential customers. One large European insurance company is even using analytics to segment millennials. Instead of segmenting this important group solely by age, the company has identified 87 different subgroups within the millennial demographic, all with different needs. Such insights are impacting this company’s product development strategies
2. Product Improvement Opportunities
Data provides early warnings about production and service problems, ultimately leading to higher quality products. By analyzing customer engagement, companies can better understand the concerns and changing desires of consumers, and innovate products accordingly. Companies can align service delivery with all functions of the business and improve production and quality control.
3. Your Competitors are Already Investing in Data Analytics
According to a survey, it is known that almost 75% of the companies worldwide have already invested or are planning to invest in big data analytics. As the competitors are already making use of this powerful tool and becoming more data-driven, this trend will soon become a necessity rather than an option for businesses to cut through the competition. Therefore, it is important that you too utilize this opportunity to stay ahead of your competitors in the market.
4. Cloud-based Solutions To Minimize Costs
When utilizing big data sets, companies can conveniently opt for cloud service providers for storage and computing power. Cloud-based solutions help companies to analyze large piles of data without any kind of investment in hardware. Consequently, most of the companies today are using cloud-based solutions to store and process huge data sets and leverage it as required for analytical applications. SovTech offers pristine Cloud hosting, Maintenance and Security Solutions.
5. Data Monetization Opportunities
Monetizing data is expected to become a hot topic in the coming years. Companies already know the value of data internally, as data is commonly known as a digital gold in the industry. The next step for the organizations would be to maximize economic benefits from the collected data with the help of external sources, partners, suppliers, and customers. The opportunities to derive value from data are abundant and have to be tapped by the businesses.
Tips For Conducting Data Analysis?
The few key steps and below are imperative when it comes to Data Analysis and the payoff is finding results!
- Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
- Posing Questions: Figure out the questions you would like answered by the data. For example, will my app reach my ideal client by offering it on the Android platform only? Figure out which data analysis tools will get the best result for your question.
- Data Collection: Collect data that is useful to answer the questions. In this example, data for your mobile app might be collected from a variety of sources like Google Play Console, Apptrace and more.
- Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. Data has to be cleaned and converted so that data analysis tools can use it efficiently.
- Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.
A dedicated product scientist is imperative to take you through the motions above implementing what is relevant to your business.
The Product Scientist is SovTech’s answer to helping our clients understand specific data and analytics about their platforms and how to use this information and data to improve their products. Our Product Scientists create custom engineering road-maps determining the product’s building blocks, to enable new feature creation and expansion. This will enable clients to visualize features discovered, that will fit into the series of developmental sprints.
To conclude it is imperative to always align insights with your overall business objectives (on and offline), data by itself is a great way to see how you are performing, but without applying what you’ve learned, it has little use.
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