There’s no query that knowledge analytics generally is a vital aggressive differentiator for corporations, delivering insights that may assist increase gross sales and market share. However how a lot enterprises acquire by analytics can rely an amazing deal on how effectively they’re benefiting from the most recent applied sciences, and the way ready they’re for future developments.
Listed here are some recommended finest practices for getting essentially the most out of information analytics endeavors.
Reap the benefits of self-service analytics
Self-service analytics permits enterprise customers to carry out queries and generate reviews on their very own, with minimal or no assist from IT and with out the necessity for superior analytics expertise. They’ll leverage easy-to-use enterprise intelligence (BI) instruments which have fundamental analytics capabilities.
A self-service analytics strategy may help fill the hole created by the scarcity of educated knowledge analysts, and might get knowledge on to the customers who want it essentially the most in an effort to do their jobs.
Enterprise customers could make choices primarily based on their evaluation of information, with out ready for knowledge scientists or different analytics specialists to generate reviews. This generally is a big profit for corporations that want to maneuver rapidly to adapt to market modifications or to shifting buyer calls for.
Step one in deploying self-service analytics ought to be to completely perceive the consumer neighborhood, together with what data necessities they’ve and what instruments they may want, says John Walton, senior options architect at IT consulting firm Laptop Activity Group.
“Data customers and govt stakeholders require a really completely different analytic instrument suite than knowledge scientists, and it’s vital to align instruments with enterprise necessities,” Walton says. “Additionally, self-service analytics is very depending on clear knowledge. If an data stakeholder loses belief within the dashboard they’re utilizing, it’s actually arduous to get their belief again. They’re going to say, ‘I don’t imagine what I’m seeing,’ and it goes south from there.”
It’s additionally a good suggestion to determine data consistency by a knowledge governance initiative, Walton says. “As soon as that is in place, you should utilize a dimensional knowledge structure because the ‘plumbing’ for self-service analytics,” he says.
In such an structure, the important thing efficiency indicators and measures displayed on a dashboard have been pre-computed primarily based upon authorised enterprise guidelines, related to the suitable enterprise filters or dimensions of study, and saved within the database. The analytics instrument consumer doesn’t must do all of this heavy lifting, Walton says.
Deploy machine studying capabilities
Machine learning will require a different architecture than analytics, Walton says. “Here you don’t want to apply pre-computed metrics that will skew the data and obscure potentially valuable insights,” he says. “ML wants to crawl through a vast amount of very granular data, most likely within a relational database, to most effectively apply its capabilities.”
For example, in the health insurance sector, a company might be dealing with massive data sets of claims data, patient encounter data, and both structured and unstructured notes.
A best practice for machine learning is to use the right layer of data for the right purposes, Walton says. “The bottom ‘ingestion’ layer is all the data coming in from your different sources, the rawest data that’s ideal for ML,” he says.
The middle, or “conformance” layer is where data has been taken from various sources and conformed to standards according to established data governance rules, Walton says. The top layer, composed of a series of focused data marts, is ideal for analytics, he says.
Manage data end to end
Many organizations are struggling to manage enormous and growing volumes of data from a variety of sources, and this can hinder analytics efforts. Deploying technologies to help manage data across the enterprise can provide a solution.
Healthcare supply company Paul Hartmann AG is using a central management platform from SAP, called Data Hub, to unify, access, and analyze data across multiple internal and external sources. The goal is to maximize the potential of data and gain the necessary insights needed to optimize manufacturing and supply chains, says Sinanudin Omerhodzic, CIO and chief data officer.
“With access to these findings, we can and keep our customers stocked with the products they need at any given time, ultimately saving patient lives,” Omerhodzic says.
By leveraging the Data Hub technology, Hartman was able to establish a “single source of truth” for customer, supplier, and operational data, helping it to better understand customer challenges.
The company is now in a position to better leverage technologies such as artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics. And it can potentially use new data sources on factors such as weather and epidemics to better predict demand at hospitals and pharmacies and ensure that they have the supplies they need at the right time and in the right amounts.
Educate business users about overall data strategy
The business users who will be leveraging data insights need to understand the company’s strategy for data science, AI, machine learning, and data analytics overall. That way they’re more likely to make sense of what they’re seeing.
“Conduct discovery sessions so that business and operational leaders understand the benefits of AI and ML,” says Venu Gooty, global practice head of data sciences and analytics at HGS Digital, a digital transformation consultancy that helps organizations use data to elevate their customer experience.
“This is particularly important for organizations embarking on the data science journey for the first time,” Gooty says. “The biggest hurdle [HGS Digital] faced when implementing [AI and ML] was to educate the business users about the outcomes attained after delivering data science projects, and to explain our approach to delivering data science projects,” he says.
Organizations need to have a data strategy in place that explains how different departments work together, Gooty says. “This is required because ML initiatives require working with multiple departments,” such as marketing, IT, operations, and others, he says.
Machine learning involves working with large volumes of data, Gooty says. For example, in order for a retailer to predict customer churn, it needs many data sets such as customer demographics, purchase history, products purchased by the customer, etc.
“These data sets typically come from disparate data sources and there may not be a consolidated source to pull the data,” Gooty says. “So the team will have to work with different departments to get the data into a consolidated platform. In organizations where data strategy and data governance is defined, this is a much more seamless process than in organizations with no clear data strategy.”
Leverage analytics in the cloud
As with just about anything else in IT, the cloud offers cost-effective and efficient options for data analytics. It’s especially beneficial for organizations that need to analyze massive volumes of data and don’t have the internal capacity to handle the demands.
Any company that’s planning to perform analytics in the cloud should first define a clear migration strategy, Gooty says. “For most organizations, this will be the first time data is moving to cloud,” he says. It’s best to start small, learn from the experience, and make changes as needed, he says.
Also, define a clear governance framework with security policies. “Moving to cloud means moving internal and external data and users to cloud,” Gooty says. “The security and privacy policies must be clearly defined, and the owners of each section must be clearly defined. The right level of access needs to be provided for each user.”
Another good practice is to automate as much as possible, Gooty says. “The power of cloud is agility and automation,” he says. “There will be a lot of requests to do manual or one-time loads, and it’s better to push back as these one-off requests adds up.”
Establish an analytics center or excellence
Organizations form centers of excellence (CoE) to provide leadership, share best practices, develop research, and offer training in a particular area of focus. Given the important strategic role of data analytics today, a CoE focused on these efforts makes a lot of sense.
A 2019 survey of CIOs and other senior IT executives in the U.S. by research firm International Data Corp. (IDC) showed that 93% said their organization is using some form of CoE to drive AI and data science initiatives. “The center of excellence is the primary hub for all things AI, BI, and analytics,” says Serge Findling, vice president of IDC’s IT Executive Programs. “As an organization with both central and distributed resources, it focuses on enterprisewide coordination.”
Global consulting firm Keyrus notes that to get the best return on investment and the most value from its data, an organization should establish an analytics CoE. The CoE streamlines all of the analytics efforts at the organization.
“Imagine a highly capable team of experts that knows your organization from within and is well-acquainted with your data sources,” the firm says. “This team possesses the skills and capabilities to leverage the data at your disposal to steer all of your efforts in the right direction.”
Keyrus says an analytics CoE should provide functions such as defining the organization’s analytics vision, including selecting tools to use and determining which key performance indicators (KPIs) are needed; building a technology blueprint; establishing standards for areas such as how to share data sources; managing programs and controlling funding; developing user skills; and organizing methodology leadership.
The firm notes that no two CoEs are the same, and how the group is structured might depend on the size of the company, its industry, its goals, and other factors. The CoE should be tailored to an organization’s specific business objectives and organizational structure.
Copyright © 2021 IDG Communications, Inc.