Too often, business decisions are made without the use of data. Enterprises of all sizes need to implement an effective enterprise analytics strategy in order to make informed decisions that will help them improve their business performance. To help you get started, this blog has outlined the key components of such a strategy, as well as provided suggestions for how to put it into action. By understanding the needs and technologies involved in enterprise analytics, you can begin to develop a roadmap that will lead to improved decision-making.
Why is enterprise analytics important?
You cannot improve what you cannot measure. Enterprise analytics is crucial for improving business performance and decision-making. By understanding how customers are behaving, you can identify problems and solutions much faster. This allows you to save time and money in the long run. Additionally, analytics can help you close the gaps and improve performance.
The components of an enterprise analytics strategy
An enterprise analytics strategy is essential to success in business. By implementing the right components, you can gain actionable insights that help you make informed decisions and improve your bottom line.
Here are the key components of an effective analytics strategy:
- Data Collection & Warehousing
- Deriving actionable insights
- Real-time collaborative decision making
It’s important to make your strategy centric to your internal stakeholders by empowering them to act on the insights in the most intuitive manner possible.
Processes for creating an effective enterprise analytics strategy
Without an effective process behind your enterprise analytics strategy, it’s virtually impossible to make informed decisions and improve business performance.
To get started, first identify all key data sources – internal and external – that will need to be measured and analyzed. After that, develop a roadmap for the project, which will include how long it will take and what needs to be achieved. Next, choose an appropriate reporting framework that captures the data in a usable and actionable format. Finally, create standard definitions for key terms so everyone understands the data being captured. Once everything is in place, it’s time to put everything into action by implementing proper processes and procedures.
This will ensure that data is captured accurately, processed in a timely manner, and analyzed to produce actionable insights.
Technology needs for enterprise analytics
It is all about reducing the gap between people and information. Technology solutions should be simple and based on the fact that business transactions are best understood by those who participate in them, the first step is to integrate the business transactions into the solution.
It should be helpful, presenting only the information necessary without a lot of unnecessary “just in case” details. More importantly, it should adapt to the needs of the business and evolve in line with the organization itself.
Imagine a scenario where you are a business user and need a quick report for a board meeting. If you are a director of sales, for example, you might want to compare the sales of products from last year with this year to see how sales have changed. To do this kind of analysis, you normally need IT professionals to run queries and generate reports in a data warehouse or on your CRM.
The traditional approach is typically reliant on batch processing the data. For instance, you will run the report and then wait for the IT department to provide it to you. If you want to compare with the other sales reports, you will have to manually or programmatically combine them together.
In contrast, a modern and business user-friendly BI application like Conbi enables you to simply ask your questions and analyze data without having to be in front of a computer all day or wait for the IT department.
Now let’s imagine you want to quickly create a dashboard and discuss the insights with your colleague to get his feedback. In the traditional way of doing this- you would need the IT department or your analyst to build the dashboard. Then you would have to log in to the data warehouse and load it onto your browser (most likely a PC or laptop). Take screenshots of charts from your dashboard and paste it into slack or a google slide which then requires you and your colleague to go back and forth. Sometimes, you would also have to re-make the report and repeat the same process again. That is why collaboration is key to data-driven decision-making.
Often users show their analysis to other team members or colleagues to get feedback this helps in collectively deciding on the best course of action for the current business realities. This is one of the primary approaches of data-driven decision making and emphasizes the importance of data sharing, data management, and most importantly, data discovery – enabling data and information sharing across an organization’s departments. Most of the BI tools deployed within enterprises do have these capabilities, but it is often a multi-step process, one should look to integrate them seamlessly and without interruption.
It’s no secret that big businesses are constantly looking for ways to improve their performance. And data analytics is a key part of this equation. But implementing an effective enterprise analytics strategy isn’t easy. That’s why it’s important to start with careful planning. Once you have a clear vision for how you want your data to help you, it’s time to invest in the right tools and technologies. This will allow you to make the most of your data insights and optimize your business performance. In the end, an effective enterprise analytics strategy helps you stay ahead of the competition. So, don’t delay – get started today and see the real benefits for your business!
Frequently Asked Questions
What are the key factors to consider when implementing an analytics strategy?
Analytics is a key component of any business and it’s important to have a clear understanding of your business goals as well as the data that will help you achieve these. You should use predictive modeling and other advanced analytics techniques in order to make sound decisions quickly. Once you have this knowledge, formulate a strategy that fits your needs and tailored towards your specific industry or company. Make sure to involve all stakeholders – from top management to front line workers – in order to get buy-in for the new analytics initiative.
How can I measure the success of my enterprise analytics initiative?
Measuring the success of enterprise analytics can be tricky, but there are a few key metrics you can use to measure success. Other key metrics include improving customer satisfaction, speeding up decision making, reducing risk and measuring adoption amongst your stakeholders. However, before you track any of these metrics, make sure that everyone in your business is on board with the initiative and understands how it will benefit them. Once everyone is on board, then measuring success becomes easier.
What are some best practices for data analysis and visualization in an organization?
Data analysis is an important step in any organization’s data-driven initiative. By collecting, storing, processing, and analyzing as much data as possible, you can make better decisions and insights. When it comes to data visualization, making your data easy to understand is crucial.
Although traditional BI tools were designed for self-service, many deployments of these solutions ended up being “report factories” where the semantic layer was used by IT experts or Analysts to create reports for others. This often reintroduced the bottlenecks and frustrations that self-service BI was supposed to get rid of.