The transformation of raw data into actionable insights in 5 steps
We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.
In a world of proliferating data, every company is becoming a data company. The route to future success is increasingly dependent on effectively gathering, managing, and analyzing your data to reveal insights that you’ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in your digital transformation.
Simplifying digital transformation
The growing amount and increasingly varied sources of data that every organization generates make digital transformation a daunting prospect. But it doesn’t need to be. At Sisense, we’re dedicated to making this complex task simple, putting power in the hands of the builders of business data and strategy, and providing insights for everyone. The launch of the Google Sheets analytics template illustrates this.
Understanding how data becomes insights
A big barrier to analytics success has been that typically only experts in the data field (data engineers, scientists, analysts and developers) understood this complex topic. As access to and use of data has now expanded to business team members and others, it’s more important than ever that everyone can appreciate what happens to data as it goes through the BI and analytics process.
Your definitive guide to data and analytics processes
The following guide shows how raw data becomes actionable insights in 5 steps. It will navigate you through every consideration you might need to make about what BI and analytics capabilities you need, and every step of the way that leads to potentially game-changing decisions for you and your company.
1. Generating and storing data in its raw state
Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization’s business (sales, marketing, payroll, production, logistics, etc.) External data sources include partners, customers, potential leads, etc.
Traditionally all this data was stored on-premises, in servers, using databases that many of us will be familiar with, such as SAP, Microsoft Excel, Oracle, Microsoft SQL Server, IBM DB2, PostgreSQL, MySQL, Teradata.
However, cloud computing has grown rapidly because it offers more flexible, agile, and cost-effective storage solutions. The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3, Hadoop and Microsoft Azure.
An effective, modern BI and analytics platform must be capable of working with all of these means of storing and generating data.
2. Extract, Transform, and Load: Prepare data, create staging environment and transform data, ready for analytics
For data to be properly accessed and analyzed, it must be taken from raw storage databases and in some cases transformed. In all cases the data will eventually be loaded into a different place, so it can be managed, and organized, using a package such as Sisense for Cloud Data Teams. Using data pipelines and data integration between data storage tools, engineers perform ETL (Extract, transform and load). They extract the data from its sources, transform it into a uniform format that enables it all to be integrated. Then they load it into the repository they have prepared for their databases.
In the age of the Cloud, the most effective repositories are cloud-based storage solutions likeAmazon RedShift,Google BigQuery, Snowflake, Amazon S3, Hadoop, Microsoft Azure. These huge, powerful repositories have the flexibility to scale storage capabilities on demand with no need for extra hardware, making them more agile and cost-effective, as well as less labor-intensive than on-premises solutions. They hold structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). Sisense provides instant access to your cloud data warehouses.
3. Data modeling: Create relationships between data. Connect tables
Once the data is stored, data engineers can pull from the data warehouse or data lake to create tables and objects that are organized in more easily accessible and usable ways. They create relationships between data and connect tables, modeling data in a way that sets relationships, which will later be translated into query paths for joins, when a dashboard designer initiates a query in the front end. Then, users, in this case, BI and business analysts, can examine it, create relationships between data, connect and compare different tables and develop analytics from the data.
The combination of a powerful storage repository and a powerful BI and analytics platform enables such analysts to transform live Big Data from cloud data warehouses into interactive dashboards in minutes. They use an array of tools to help achieve this.Dimension tables include information that can be sliced and diced as required for customer analysis ( date, location, name, etc.). Fact tables include transactional information, which we aggregate. TheSisense ElastiCube enables analysts to mashup any data from anywhere. The result: highly effective data modeling that maps out all the different places that a software or application stores information, and works out how these sources of data will fit together, flow into one another and interact.
After this, the process follows one of two paths:
4. Building dashboards and widgets
Now,developers pick up the baton and they create dashboards so that business users can easily visualize data and discover insights specific to their needs. They also build actionable analytics apps, thereby integrating data insights into workflows bytaking data-driven actions through analytic apps. And they define exploration layers, using an enhanced gallery of relationships between widgets.
Advanced tools that help deliver insights include universal knowledge graphs and augmented analytics that use machine learning (ML)/artificial intelligence (AI) techniques to automate data preparation, insight discovery, and sharing. These drive automatic recommendations arising from data analysis and predictive analytics respectively. Natural language querying puts the power of analytics in the hands of even untechnical users by enabling them to ask questions of their datasets without needing code, and to tailor visualizations to their own needs.
5. Embed analytics into customers’ products and services
Extending analytics capabilities even further, developers can create applications that they embed directly into customers’ products and services, so that they become instantly actionable. This means that at the end of the BI and analytics process, when you have extracted insights, you can immediately apply what you’ve learned in real time at the point of insight, without needing to leave your analytics platform and use alternative tools. As a result, you can create value for your clients by enabling data-driven decision-making and self-service analysis.
With a package like Sisense for Product Teams, product teams can build and scale custom actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.
Author: Adam Murray