Data-driven decision making is a key attribute of modern digital business. But experienced data analysts and data scientists can be expensive and hard to find and retain.
One possible solution to this challenge is to implement self-service analytics, a type of business intelligence (BI) that allows business users to run queries and generate reports on their own with little or no help from data or IT specialists.
Self-service analytics typically involves tools that are easy to use and have basic data analytics capabilities. Professionals and business leaders can take advantage of these to manipulate data so they can identify market trends and opportunities, for example. They do not need to have analytical experience or a background in statistics or other related disciplines.
Given the current gap between the demand for experienced data analysts and the supply of these professionals, and the desire to quickly get valuable business insights into the hands of the users who need it most, it’s easy to see why companies would find self-service analytics. attractive.
But there are right and wrong ways to implement and use self-service analytics. Here are some tips for IT leaders looking to deliver on the promise of self-service analytics strategies.
Have a clear and complete analysis plan
Data analytics and analytics tools have gained such a high profile within many companies that it’s easy to see how they can be overused or applied inappropriately. This is an even bigger problem with self-service analytics, because it allows a much broader range and base of people to analyze the data.
That’s why it’s important to establish a plan for where and when it makes sense to use analytics, and have reasonable controls in place to prevent your analytics strategy from becoming a free for all.
“Determine your mission, vision, and the questions you need to answer about analysis before you even begin,” says Brittany Meiklejohn, a sales and business process analyst at Swagelok, a developer of fluid systems products and services for oil, gas, chemicals and clean energy industries.
“It’s extremely easy to get caught up in all the charts and graphs you can create, but that gets overwhelming very quickly,” says Meiklejohn. “Having that roadmap early on helps to cut back and focus on the actual metrics to create. Also have a data governance plan to validate and keep the metrics clean. As soon as a metric is not accurate, it is difficult to gain acceptance again, so it is extremely important to routinely confirm the accuracy of all analyses.”
The analytics plan should emphasize the use of proactive data as much as possible, Meiklejohn says. “Approach [on] data that is actionable and can be implemented back into the business,” he says. “Incorporate learning to transform processes and decision-making at an organizational level. It’s great to understand the historical side of the business, but it’s hard to change if you only look at the past.”
At Swagelok, departments use self-service analytics tools from Domo to determine if customer orders will be late, schedule production runs, analyze sales performance, and make supply chain decisions.
“We have seen an increase in efficiency; everyone can get the data they need to make decisions much faster than before,” says Meiklejohn. “We are making more responsible decisions based on data, as each department is using the data for decision making.”
Go for quick wins
While it’s important to have a long-range analytics strategy, that doesn’t mean organizations need to move at a fast pace with self-service analytics.
“At my previous company, our advanced materials business had a saying: ‘Go fast, take risks and learn,’” says Keith Carey, CIO of Hemlock Semiconductor, a maker of products for the solar power and electronics industries. “That would be my advice to those just starting out. [with self-service analytics]. Don’t get me wrong, governance is very important and it can come a bit later so as not to stifle creativity.”
It’s a good idea to find a small task force “and assign a mission to the moon to demonstrate the art of the possible,” says Carey. She suggests that teams focus “on the data pipelines that drive business logic and consistent metrics across the enterprise. Understand the importance of timeliness and quality of data on which important decisions are made. That’s a great place to start.”
Hemlock launched a self-service analytics initiative in 2018 using Tibco’s Spotfire platform, which is currently used by all of the company’s functions. “Before that, IT was developing custom .NET applications that handled the data and provided the initial charting capability,” says Carey. “The most popular feature of these applications was an ‘export to Excel’ button, where [the Microsoft spreadsheet] it became the preferred analytics platform.”
A handful of the company’s brightest engineers also created macros that would combine new data sets, “that took all night to run on someone’s PC,” says Carey. “And hopefully, if it didn’t crash, the dataset was shared among engineering professionals.
With self-service analytics capabilities, Hemlock has seen benefits such as faster decision making and quicker results. Self-service enables all functions, including operations, finance, procurement, supply chain, and continuous improvement teams, to perform data discovery and create powerful visualizations.
“We shortened the learning curve, delivered results faster, and accelerated our understanding of our manufacturing processes, which led to improving our products and reducing costs,” says Carey. “In a very short time, we saved millions of dollars by improving existing reporting methods and discovering new insights.”
Take advantage of natural language processing
Natural language processing (NLP) makes analytics more accessible to more people by removing the need to understand SQL, database structures and the concept of joining tables, says Dave Menninger, senior vice president and director from Ventana Research.
There are two main aspects of NLP when it comes to analytics, Menninger says: natural language search, also known as natural language query, and natural language presentation, also known as natural language generation.
“Natural language search allows people to ask questions and get answers without [any] special syntax,” says Menninger. “Just like typing a search into a Google search bar, you can type, or in some cases speak, a query using everyday language.”
For example, a user might request to see the products that had the biggest increase or decrease in sales for that month. Results would be displayed and the user could then refine the search, for example, to determine available inventory for certain products.
Natural language presentation deals with the results of the scans rather than the query part, Menninger says. “Once a query has been formulated, using NLP or otherwise, the results are displayed as narratives explaining what was found,” he says.
In the product example, instead of displaying a product graph showing sales increases or decreases, the natural language presentation would generate a few sentences or a paragraph describing specific details about the products.
“People have different learning styles,” Menninger says. “Some like tables of numbers. Some prefer graphics. Others do not know how to interpret tables or graphs and prefer narratives. The natural language presentation makes it easy to know what to look for in an analysis. It also eliminates inconsistency in the way data is interpreted by explaining exactly what should be removed from the analysis.”
Use built-in analytics
Embedded analytics involves integrating analytical capabilities and data visualizations into business applications. Embedding real-time dashboards and reports into these applications allows business users to analyze the data in these applications.
“Integrated analytics brings analytics to the applications that people use in [their] everyday activities,” says Menninger. This could include line-of-business applications such as enterprise resource planning (ERP), customer relationship management (CRM), or human resource information systems (HRIS), as well as productivity tools such as collaboration, email, spreadsheets, etc. , presentations and documents.
“In the context of business applications, prebuilt analytics make analytics much easier to access and use for line-of-business personnel,” Menninger says. “It also provides good governance, as the data is managed by the underlying application where access rights are already held.”
The difference between success and failure with self-service analytics can come down to the technology tools that companies choose to implement. Business executives must work closely with IT leaders to evaluate tools and determine which ones best meet the organization’s needs and fit within its infrastructure.
Among the requirements that financial services company Western Union had when selecting a self-service analytics platform were that it be easy to integrate with multiple disparate data sources, be flexible and easy to use, have powerful analytics capabilities, and have requirements infrastructure minimums.
The company implemented a Tableau platform to allow business users to make decisions based on their own queries and analysis in a governed environment, says Harveer Singh, Western Union’s chief data architect and head of engineering and data architecture.
Business departments can create their own queries and reports and collaborate without the need for IT support, Singh says. “Users have the freedom to slice and dice the data without technical knowledge,” she says. “Data can be derived from multiple sources in various formats.”
When organizations select the right analytics tools, self-service analytics “empowers business users to retrieve and analyze data without the need for IT experts/product specialists for report development and analysis,” says Singh. It is an asset “that responds to dynamic business requirements”.
Source: news.google.com