6 Features to Look For in Big Data Analytics Tools
Information is power. And there's great potential for that information to come from data. We create 2.5 quintillion bytes of data every day - but unless we process it and make it available for action, it's useless. Even then, it's incredibly difficult to get insights in the right hands when they need it.
This is where big data analytics tools change the game.
Creating a cohesive data strategy can be an overwhelming undertaking - but it can have very high rewards. Data analytics software has changed to make data analysis much more manageable ... if you know what to look for.
Here are six features to look for that will make your life easier and help you get insights faster.
6 Features to Look For in Big Data Analytics Tools
1. Can connect to data from diverse sources.
Your data lives in multiple sources, but to make your data useful, the first step is to extract it reliably. Your data stack must be able to pull the data from its original source for further processing or storage.
Pull historical and live data
It’s important to note that some data changes continuously, especially when extracted from live sources. So the way that you ingest or extract the data must contain critical information for historical value and comparison. Such as when the data was last updated and who updated it.
Data source types
Data sources can vary from documents, spreadsheets, software, SaaS applications, 3d Files, APIs, NoSQL sources, transactional systems, relation databases, on-premises databases, cloud databases, and more.
2. Is a cloud based data lake- can access raw data.
Data is not useful if you or other teammates can't access it. Every successful data pipeline needs a data warehouse or data lake to to store data.
Cloud based data enables greater flexibility
Utilizing a cloud software enables you to access your data over the internet, which is synchronized with other information over the web.
Data warehouse vs. data lake
The difference between a data warehouse and a data lake is that a data warehouse hosts pre-formatted data ready for insights while a data lake hosts unformatted data.
The benefit of utilizing a data lake is that you do not need to clean or process your data before uploading increased access to data. By utilizing a data lake you eliminate the need for an additional data warehouse and additional revisions and the errors that come with the additional complication. With a data lake all your data can be stored and within a single platform.
3. Goes beyond data transformation - into smart data formats.
It’s not enough to have the data available. It must also be clean and compatible between applications, systems, and types of data. The data must be processed or transformed to ensure it is ready and easy to use in analysis. Poorly formatted data will cause delays in data processing. This can be a costly and resource-intensive process.
Data transformation includes processes such as data type conversion, filtering, summarizing data, anonymizing data, and more.
Smart data makes data blending and analysis simple
Smart data is data that is formatted and classified so that it can easily be reformatted and compared for analysis. Smart data can be pre-formatted data or recognized data types. Imagine you are working on a construction project with various measurement types. You can eliminate conversions and conversion errors if the measurement can be pre-classified and transformed according to your end requirements. It's an important step that saves valuable time, reduces conversion errors across data sets, and enables data blending.
Data blending makes the data more meaningful and substantial by merging different data sources such as third-party data with an existing data source –enabling usability for decision-making. One example would be using internal sales data with advertising data to measure the effectiveness of a marketing campaign.
4. Enabled to perform data visualizations.
Big data analysis requires easily digestible reports and data. Visualized data makes data easier to understand and detect patterns. They help you make the message more clear by attributing visual elements to complicated data sets.
Visual elements include charts, graphs, tables, and more. Visualizations are essential to a business or project's success and aid decision analysis to effectively present data analysis.
Thus modern data stacks require interactive reports and dashboards with stunning charts and graphs. The visualizations simplify data analysis and facilitate faster and better-informed business decisions.
5. Can be automated and reusable.
A major pain point to conventional data pipelines is that each pipeline must be re-built to rerun data. New technology enables the automation and reuse of dataflows and data pipelines from data connection to synchronized reporting. This automation saves time, creates consistent formatting, and eliminates errors.
A dataflow diagram is traditionally a representation of how your data connects, but no-code interfaces instantly showcase and connect diverse data for deeper and more straightforward views. An added benefit of no-code tools is access to non-technical users to data and easier understanding between different data sources and how they connect.
Taking it a step further, the best part about no-code dataflows is that they live and sit on top of your data. It’s easy to create a prebuilt data app and replace data for consistent data flows and reports with just a few clicks.
6. Share synchronized reports to various stakeholders.
It's essential to have tools that provide meaningful context to the data, presented in a way that facilitates stakeholders’ data-driven decisions. It’s not enough to have data and raw visualizations. The context that stakeholders derive value from must be obvious to be helpful. Look for software that can create multiple reports with curated views for individual stakeholders.
Enabled to provide instantaneous insights as data changes
It's not enough to have snapshot reports. The data in the reports must be accurate and instantly synchronized across the various views as the data changes. To facilitate actionable insights, the data must be accurate and up to date for everyone- save time and reduce errors with software that enables you to sync data across reports instantly.
Embeddable results for real-time analytics
It's not enough to have reports that can sync data. These reports must be made available in the way stakeholders ingest their data. This means that the software must be flexible enough to embed reports to be viewed in the proper context required. Some teams like to see embeds in Notions, SharePoint, wikis, and more.
Thanks to new big data tools, organizations can do more than ever before. We know data will only get bigger and tech more sophisticated as the months—not years—roll in. You either get up to speed with big data statistics or run the risk of losing out on valuable, insightful data for decision making.