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Analytics play a crucial role in empowering business leaders to make strategic, data-driven decisions. They help businesses avoid potential pitfalls, and stay ahead of the competition. Today's business intelligence (BI) and analytics platforms are much more than mere reporting tools. They have a wide range of features and capabilities to offer. If you're a business owner looking to invest in one of these platforms, it is important to know some basic analytics systems terminologies to bring context to your research, and ensure you choose the right product for your business. 

Here are the basic analytics terms you should know:

Big data 

Every time you open and interact with an app or run an internet search, your actions generate data in various forms, including images, text, URLs, videos, and more. Similarly, data is being gathered across the world from a variety of internet-enabled sources (mobile phones, electronic devices, social networks, GPS devices, server log files, and more). This massive amount of data produced—often referred to as "big data"— is impossible for traditional data management systems to handle. Only advanced big data solutions can efficiently process and analyse big data, so your business can derive insights and make smarter decisions.

Business Intelligence 

Business intelligence (BI) denotes tools and processes that analyse large volumes of raw data sets and provide actionable insights. Many BI vendors offer software with built-in AI-powered forecasting and reporting capabilities to help organisations discover hidden insights and patterns, and make strategic, data-driven decisions. 

Check out our blog to learn more about how business intelligence software can help your organisation.

Dashboard 

A dashboard is a user interface that summarises all the metrics or key performance indicators (KPIs) you want to track. It shows you the big picture at a glance. Most BI tools now offer intuitive drag-and-drop tools so users can personalise their dashboards according to their preferences.

Data sourcing 

BI solutions allow users to import data for analysis by integrating and gathering information from various internal and external sources. This process is called data sourcing. Sources can include in-house or public cloud databases (like Amazon RDS and Microsoft SQL Azure), online storage services, flat files, feeds, web URLs, surveys, and more. As a business owner, you may also have a lot of valuable information stored in your CRM, accounting systems, help desk and other business applications. To make the most of it, it's critical to choose a BI platform that can source and analyse information from multiple applications and verified data sources.

Data storytelling 

Data storytelling is the ability to present information through captivating narratives and visualisations. This function can be a key differentiator that makes data easy to comprehend and act upon. In recent years, people have become more selective of how they engage with and consume information. In response, BI suppliers are concentrating on giving users a more immersive experience, rather than throwing a bunch of numbers at them. The below infographic from Budget 2022-2023 is a good example to mention here. To image givers viewers a visual breakdown of the Commonwealth water infrastructure investments planned for the year, highlighting the geographic location where the money would be invested.

Screenshot taken from budget.gov.au

Data cleansing 

Data cleansing, also known as data scrubbing, helps identify and correct inconsistent and duplicate entries to prepare your data for analysis. When working with large volumes of data, it can be tough to identify and correct these errors manually. Even a minor misstep, such as adding a simple hyphen or additional space during data entry, can cause the system to misinterpret, impacting the outcome of your analysis. To avoid this type of issue, it's important to have a BI tool that helps you keep your data cleansed regularly.

Structured and unstructured data

Structured data is information (typically in the form of numbers, letters, and values) stored in an organised and well-defined pattern. The data is arranged in a way that is easy to discover and analyse. Unstructured data includes qualitative information not set in a pre-defined format. Unstructured data formats often make analysis and processing challenging. Most of the data generated today (images, videos, emails, blogs, presentations, social media pages, etc.) is unstructured.

Let's say you receive emails from prospective customers daily enquiring about a few products. At a later point you may want to analyse and know more about common queries you receive. While you can easily keep track of the sender information, date, and email subject, it may not be easy to sift through hundreds of messages to compare and analyse all the unique descriptive requests found in the email's body content. But with the advancements in artificial intelligence, many BI platforms can now extract useful insights from unstructured data to provide businesses with a deeper understanding of their customers.

Collaborative analytics 

A project's success is often dependent on effective collaboration. Collaborative functions on an analytics platform help teammates quickly share reports and dashboards with predefined permission levels, and participate in related discussions. This helps save time, avoid miscommunication, and encourage faster decision making.  

Augmented analytics 

Augmented analytics use the potential of machine learning and natural language processing to improve the way people explore and analyse data. With augmented analytics, many platforms now provide interactive capabilities, where you can type in a question and receive personalised answers and reports. For example, in Zoho Analytics, our built-in AI assistant, Zia, can easily understand your text and voice inputs and retrieve information from our other business applications in seconds. If you tell Zia, "Show me the revenue for this month," it will instantly generate a revenue report. You can then ask specific questions about regional revenue, or anything else that interests you. This function can be particularly useful if you aren't familiar with data structures.

Screen grab from Zoho Analytics

Embedded analytics 

With embedded analytics, you can seamlessly integrate complex data analytics into other applications. Whether it's your CRM, help desk, or accounting tool, BI platforms can embed AI and machine learning into your business applications, so you can gain contextual insights straight from your workflow without switching back to your analytics tool. This can save a lot of time and help simplify your decision-making process.

We hope this article helps you get familiar with the concept of business analytics so you can make more strategic and informed decisions. If you're interested in learning more about this concept, we encourage you to check out these articles:

Artificial intelligence and why it matters for your business

Importance of clear data analysis in making business decisions


 

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