Business Analytics: how data analysis can transform your business
Business Analytics is the practice of repeated and systematic exploration of an organization’s data, emphasizing statistical analysis. It is used by companies committed to data-driven decision making.
We have already dealt with this topic in articles, and we also have an ebook that goes in-depth on the subject. Now we’ve decided to dig into this idea a little more and explore how a good Business Analytics strategy can help transform your business.
Why invest in a Data Analysis strategy
Data is the new currency. It is strongly impacting people’s lives and, consequently, their businesses.
An explosion of consumer data allows companies and brands to make targeted marketing campaigns and develop or sell more differentiated products more efficiently. With a good data strategy, they can offer personalized experiences and recommendations to customers. This enhances business and increases profitability.
The internet, one of the most transformative innovations of the modern era, was the catalyst. Consumers take advantage of connectivity for entertainment, education, knowledge, social sharing, and shopping.
In particular, when it comes to shopping, the web has put more power in the hands of consumers. Brands no longer control information about their products and services. Instead, informed users enter into business transactions with details about what they want and what they should pay for.
In short, investing in a Data Analysis strategy means keeping up with the current digital transformation and having more ways to make better decisions. It is also possible to have more predictability to help direct business models towards a more promising and profitable future.
How Business Analytics builds a Data Strategy
Business Analytics techniques are divided into two main areas. The first is basic business intelligence, which involves examining historical data to get a sense of how a business department, team, or team member performs at a particular point in time.
The second area of Business Analytics involves a deeper statistical analysis. This can mean doing predictive analysis by applying statistical algorithms to historical data to predict the future performance of a product, service, or website design change.
Or, it could mean using other advanced analysis techniques, such as cluster analysis, to group customers based on similarities between various data points. This can be useful in targeted marketing campaigns, for example.
In practice, Business Analytics is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies see them as a corporate asset.
In this sense, by having a good strategy and using good Business Analytics platforms, companies can organize themselves to take advantage of the data’s potential. By providing detailed data analysis to executives, decisions are based more on insights and less on intuition. The “trial and error” dimension is replaced by “discovery and action.”
Advantages of a Data Analysis Strategy
A successful data analysis strategy improves data quality, as long as qualified analysts do it. They understand technologies and business and boast an organizational commitment to data-driven decision making.
To understand the benefits that companies obtain by undertaking a data analysis strategy, we will start by looking at the three methods of Business Analytics methods that are most used:
- Descriptive analysis: tracking the main performance indicators to understand the current state of the business;
- Predictive analysis: analysis of trend data to assess the likelihood of a future outcome;
- Prescriptive analysis: using past performance to generate recommendations on dealing with similar situations in the future.
Generally speaking, the advantages that companies obtain are those listed below.
Proactivity and anticipating customer needs
Organizations are under immense pressure not only to focus on acquiring customers regularly but also to understand customer demands to optimize the customer experience while developing a lasting relationship.
When customers share their data, they expect their favorite brands to provide a better experience, form the necessary involvement, and provide seamless interaction across multiple touchpoints.
That is why companies must capture and take advantage of the various customer identifiers, such as email addresses, physical addresses, cell phone numbers, etc. Customers are using multiple channels to interact with your product. They expect companies to offer relevant, real-time experiences.
Delivery of relevant products
There is no doubt that products are the lifeblood of all organizations. They are also undoubtedly the most significant investments that companies make.
The product management team’s role is to discover current market trends, responsible for driving the strategic maps of innovation, services, and features.
Therefore, effective and adequate data collection from third-party sources, where individuals display their opinions and thoughts when merged with analysis, helps companies to remain competitive. Even when new requirements or new technologies appear and shake up the market again.
Optimizing the customer experience
If operations are poorly managed, this can lead to several costly problems. Also, it involves the risk of damaging the customer experience and harming brand loyalty.
When companies apply data analysis to projects, the controls of processes, and the optimization of business operations to create products or services, this guarantees effectiveness and efficiency when meeting customer expectations.
Operational improvements
Also, companies can obtain better operational experience. Modern and advanced analytical techniques can be established to improve the productivity of field operations, for example.
It can also help to optimize the organizational workforce according to business and customer needs. The maximum use of data analysis helps ensure continuous improvement through the proper measurement of key operational metrics.
Mitigation of risks and fraud
The main objective of fraud and security analysis is to protect physical, intellectual, and financial assets against misuse by internal or external threats.
Effective data analysis capabilities help provide the ideal level of fraud prevention and complete organizational security.
The use of statistical methodologies for fraud-prone models, which leads to alerts, guarantees timely responses triggered by threat detection procedures and automated alerts or mitigation.
Data management, coupled with transparent and efficient reporting of all fraud incidents, can result in better fraud management. As well as build trust and provide security for your users.
Also, complete integration and correlation of all data across the company can provide a unified view across different product lines, transactions, etc.
Is Business Analytics already part of your company’s strategy? Go even deeper into this topic. Download our ebook: “Business Analytics- The data age has begun!”
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