Make data-driven decisions with enterprise data analytics | Forte Group

Make data-driven decisions with enterprise data analytics

The big data market is characterized by high value, massive volume, and rapid growth. The global enterprise data analytics market was valued at $189.1 billion in 2019 and is forecast to grow even more. Rapidly developing industries such as the Internet of Things, artificial intelligence, and cloud computing are generating increased interest in the analytics software application and business intelligence market. The banking industry alone accounted for around 14 percent of all data market revenue in 2018.

Data-driven decisions have been a reality for large enterprises for decades. Big data analysis reflects insights and helps you to understand customer behavior and upcoming trends. Growth strategies require data analytics sourced directly from your business to identify new opportunities, successful models, and more productive operations.

Source:2021 IDG data and analytics survey | executive summary

Speed and efficiency are the key benefits of big data analytics. While planning long-term decisions, companies also need to manage daily operations. Enterprise big data analytics identifies insights for urgent decisions and provides the ability to stay agile and work faster than ever before.

What is enterprise data analytics?

Enterprise data analytics drives business strategies and actions by defining the use of data and predictive modeling. It is a form of big data used for the analysis of a particular enterprise and all the data that it stores. The amount of generated data is enormous. Every second Google alone generates more than 40,000 queries. Thus, every enterprise requires a solid platform to accumulate and analyze the data, taking into account the speed of queries.

Data management, data engineering, and analytics techniques applied successfully can simulate real-life situations and forecast crises, as well as optimize production and help to program positive scenarios.

A recent business intelligence survey by MicroStrategy showed that more than 94 percent of companies around the world use data analytics to build their business transformation strategy. Among them, 59 percent use advanced and predictive analytics and 12 percent have already adopted embedded analytics via collaboration tools and email analytics. Real-time big data analytics software is widely used by production teams to keep track of customer orders and update their KPIs and metrics.

 The importance of data analytics strategy for enterprises

Enterprises face daily challenges managing big data while competition is growing. It’s essential that enterprises recognize the value of big data analytics in helping them to optimize their operations.

Three benefits of implementing big data:

1. Informed internal metrics and KPI

Being informed about internal operations provides a bigger picture on the competition map. You can compare your data with that of your competitors and add market data to set KPIs for future goals. Direct or indirect data sources can help you to extract your competitors’ statistics. Full competitor analysis can highlight the areas that need your attention the most.

2. Better customer insights

Customer analytics helps you to better understand your customers’ behavior, satisfaction, buying habits, brand loyalties, and reasons for purchasing. With the help of big data, you can predict your customers’ needs, your product lifecycle, and the correspondence of demand and supply.

3. Precise forecasting

Combining internal and external analytics lets you forecast market movements, competitors’ behavior, and your own product cycle more precisely. In 2018 Forbes produced a detailed report on how big data and analytics help industries. Just have a look.

Source: Forbes report on how big data and analytics have significantly influenced the industry

To benefit more from big data analytics, you need to build your implementation strategy. A good enterprise strategy is practical and easy to follow, related to the industry and enterprise itself, flexible to regular changes, and easy to integrate with any kind of analytical software. 

The main challenges of enterprise analytics implementation

While there are many benefits of using enterprise data analytics, the process of implementation is not without some challenges. The more highly regulated the industry, the more difficulties you will encounter. The five most common challenges are:

1. Failure to use all available data

According to statistics, enterprises use only 50% or less of their structured data for analysis.

2. Poor data management

Too much time spent on collecting data leads to not having enough time for processing and interpreting results.

3. Data control and data flexibility issues

Privacy policies and data legislation may limit the integration of real-time decisions.

4. Access issues

Security restrictions don’t allow all employees full access to data and analytical tools.

5. The gap between analytics and business processes

Business goals are not aligned with the enterprise analytics strategy, leading to misunderstandings when identifying and setting priorities.

Types of data analytics

There are four main types of data analytics. The more complex the data analysis, the more value it brings for further analysis.

Descriptive analytics

Uses historical raw data to create the summary and answer the question: “what happened?”

Pros: indicates whether everything is right or wrong.

Cons: doesn’t explain the reason and needs to be combined with other analytics methods to build a strategy.

Example of usage: analyzing the monthly revenue of a product group and comparing it with the number of items produced helps to focus on problematic categories.

Diagnostic analytics

Combines historical data with other data to answer the question: “why did it happen?”

Pros: provides in-depth insights into a particular problem

Cons: needs detailed and varied data to make a bigger picture

Example of usage: customer segmentation, combined with new filters, helps you to identify your customers’ motivation to purchase.

Predictive analytics

Uses different statistics, detects clusters and exceptions, analyses trends, and makes predictions to answer the question: “what is likely to happen?

Pros: uses more detailed and complex data based on machine learning and a proactive forecasting approach.

Cons: the estimate highly depends on the quality and stability of the data, and needs to be constantly updated and optimized.

Example of usage: analytics can predict what to expect if you change the positioning or the product package. It can also identify the right target group for a new product.

Prescriptive analytics

Creates actionable insights for business data use and determines actions to take to escape problems and follow a promising trend.

Pros: combines different approaches and types of data, and provides detailed insights with further development plan.

Cons: rather expensive to implement.

Example of usage: analytics can easily predict opportunities to repeat positive customer behavior.

What types of data analytics does your company need?

In 2020, the COVID-19 pandemic massively impacted the economy and the way many companies work. Many technical trends predicted to lead the way at the end of 2019 faded into the background, leaving the focus on basic operations and processes. BARC’s Data, BI & Analytics Trend Monitor 2021 asked 2,259 C-level users what they thought about BI and analytics trends. The survey results show the analytics and data management trends that are currently driving the market by industry.

Before choosing an analytics type for your business, it’s best to conduct your own research about data management in your organization. To start, answer these questions to help you identify the right type of analytics:

  1. What is the current state of data analytics in your company?
  2. Will data analytics identify and address your pain points right away?
  3. How deep do you need to go into the data?
  4. What results do you expect from data analytics strategy implementation?
  5. Is your company technically ready for AI forecasting?

If you match your company’s current assets with the issues you want to resolve, you may benefit from a simpler approach. Start with structuring your data and the flexibility of your analytical tools. Then continue with implementing the simplest analytics types to see how you can take advantage of each.

The bigger and more complex your business, the more you need a customized solution with the appropriate technology stack and a detailed roadmap. If your in-house team is skilled enough to build such a solution, you can start right away. But if you need to hire a consultant, it could be a long process, costing you a lot of time and effort to get them up to speed.

In this case, we recommend approaching a mature data analytics vendor with expertise in your field. The provider will help you look through your analytics’ current state, choose the right strategy, and customize a solution to meet your needs and maximize ROI.

In conclusion

In this article, we’ve explored the benefits and challenges of enterprise data analytics. We’ve highlighted the types of analytics and shared reports and our thoughts on building a successful analytics strategy that can work for you. 

Organizations need data to perform enhanced services, predictive maintenance, and real-time analytics. Yet, it’s difficult for real-world locations, such as hospitals, warehouses, and oil and gas companies to develop and deploy software to these “edge” locations quickly and inexpensively. 

Forte Group is a trusted partner for data analytics services, and we are ready to collaborate and share our expertise. Suppose you need a qualified vendor to help you with data analytics. In that case, Forte Group provides various services, from on-demand data analysis and BI consulting to developing open-source environments with remote access. 

We cover such domains as: 

  • financial analytics
  • customer analytics
  • brand and product analytics
  • manufacturing analytics

We’ll help you overcome time-consuming and inaccurate analytics and reporting by implementing data analytics best practices for improved decision quality and speed. Our dedicated team will help you optimize business performance, develop software from scratch, enhance new features, and integrate third-party tools. We begin the process with a detailed discovery phase and discuss every step of the proposed strategy. Our experts provide you with recommendations that will bring your vision to life.

By focusing on what is most important to you, we foster a path to a valuable solution and build a high value, outcome-based partnership. We do so in a way that is transparent and highly integrated.

Helen Pilchenko

by Helen Pilchenko

Helen Pilchenko is a technology writer at Forte Group. She has a solid creative mindset with deep technical knowledge and analytical skills to deliver business results.

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