Many organizations understand the power of data analytics, but don’t know where to begin
We’ve all heard the phrase “data is the new oil” to describe the exponential growth of big data platforms and analytics as key mechanisms of digital transformation. For many organizations, the importance of becoming data-literate — capable of communicating data and analytics in ways that spur innovation, enhance customer experience, increase revenue, and fulfill the more traditional role of risk mitigation — has become a fundamental business asset.
As of 2018, more than 50 percent companies are using big data analytics in their business, which shows a tremendous growth as compared to 2015, when the number was only 17 percent. And yet, according to a recent Gartner report, nearly 90 percent of organizations have low business intelligence (BI) and analytics maturity. For businesses that want to optimize their data and accelerate deployment of emerging technologies (e.g., artificial intelligence, machine learning, Internet of Things, natural language processing), this is a big hurdle to overcome.
Nearly 90 percent of organizations have low business intelligence and analytics maturity. — Gartner
Gartner found that organizations relying on spreadsheets, gut instinct, or standalone analytics projects are more likely to exhibit the following characteristics: primitive or aging IT infrastructure; limited collaboration between IT and business users; data rarely linked to a clearly improved business outcome; BI functionality mainly based on reporting; and bottlenecks caused by the central IT team handling content authoring and data model preparation.
There are many benefits to becoming a more data-informed business, such as gaining a deeper understanding of the customer journey, discovering new cost-saving measures, developing new products and services, becoming more agile and better able to forecast or respond to changing markets/conditions, better detecting anomalies that could predict and prevent risks or costly maintenance—the list goes on.
That being said, data is only as valuable as the insights that can be drawn from it. From rewiring supply chains to removing operational bottlenecks, your business could be benefiting from data analytics adoption right now, and should be. Here are six steps organizations can take to begin deriving value from a data analytics program:
Step 1: Strategy and Vision
First, identify your goals. What do you want your data to do for you? Data and analytics leaders should coordinate with IT and business leaders to develop a holistic strategy. They should also view the strategy as a continuous and dynamic process, so that any future business or environmental changes can be taken into account.
Step 2: Start Small
Even the smallest of businesses are working with a huge quantity of data—more than what is needed to gain impressive ROI from BI. Data is flowing into your organization from all directions, from customer interactions to the machines used by your workforce. For a proof of concept, decide which kind(s) of data your team will analyze: transactional, human-generated (such as social media), mobile, or machine/sensor data. Machine and sensor data that emanates from the Internet of Things (IoT) can be used to build analytical models that do continuous monitoring for predictive behavior, such as identifying when sensor values indicate a problem, and offer prescriptive directives, such as alerting a store employee to check a low temperature in a freezer. This is a use case deployed by Mission Data’s IoT platform, OpSense, as it’s tracking critical operations data and the technology not only reduces costs but assures compliance.
It’s essential to cut through the noise to manage the multiple sources of data and identify which areas will bring the most benefit. What area is key to achieving your overarching business strategy? This could be finance or operations, for example.
Step 3: Gain the Skillset
Analytics is a science, and requires a more formal approach than, say, taking a one-hour webinar. Tech professionals will benefit from an academic approach that delves into model definition and training, and will most likely require a fundamentally new skillset. No budding data scientists or analysts on your team? If your in-house analytics capabilities are lacking, consider partnering or outsourcing—however, there are trade-offs.
With analytics outsourcing, one of the biggest cons is relinquishing a certain amount of control. Carefully review your contract and make sure it clarifies who owns the logic or algorithm, and upon exit, who owns the data, models, approaches, framework, and configuration. Another major concern is data security, and along with that comes issues surrounding data governance, intellectual property (IP), ownership, liability, and more.
The trend towards the creation of a chief data officer is increasing. This could be incredibly beneficial; one person to head your data collection and analytics department and digital strategy would be a crucial component to the growth and long-term success of your company.
Step 4: Adopt Data Governance Enterprise-Wide
The continuing problem of data breaches coupled with increasing regulations such as GDPR and the California Consumer Privacy Act are symptoms of the broken trust between companies and customers. Effective governance of data should be a priority for organizations in 2019 as they balance the opportunities and risks of a digital environment. Companies must become true custodians of data, and data governance must be driven by customer and stakeholder needs.
This requires an end-to-end strategy that includes data management and use of analytics. Gone are the days of tossing large amounts of data into data lakes without first applying data catalogs and metadata tagging processes. The evolution of data management has even spawned a new practice—DataOps.
Mission Data's OpSense platform monitors a range of operational data.
Step 5: Create an Integrated Analytics Platform
Once companies have empowered their data analytics teams, they can refine that raw data and apply statistical and analytical methods, beginning a symbiotic relationship between machines and humans to inform decision-making. To analyze the data effectively, you’ll likely need integrated systems to connect all the different data sources.
As data science platforms gain momentum, analytics teams are turning to these open source and commercial tools to support a broad range of uses at scale. Capabilities range from advanced data mining, preparation, and visualization to machine learning to build and deploy predictive data flows. Consider analytics platforms that offer an intuitive user experience so that a variety of stakeholders can access data, which can help connect the entire workforce and makes for a more unified organization.
Step 6: Turn Insights Into Action
Today’s data analytics tools pull together complex sets of data in ways that make the information digestible for decision-makers. It’s not just about having a sleek dashboard, rather, the platform should present synthesized insights in ways that make reporting and planning easier, telling users which actions need to be taken.
For example, Mission Data created a data analytics platform for The Atlantic to measure key performance indicators for its digital readership. Instead of using a convoluted spreadsheet, The Atlantic’s editorial, advertising, and product teams were given access to real-time data and predictive analytics, allowing them to better measure article performance and respond to changes immediately. The publisher’s operations team can now track their target audience and derive value from the data as they continue their growth as the third-longest-running magazine in America.
Becoming a more data-driven organization is easier said than done. With the potential of artificial intelligence, data science is constantly changing and advancing. Data analytics is a journey that never ends. As data pours in, company priorities change. And as new tools become available, organizations will have to adjust their big data approach and revisit strategies and efforts. The best data-driven solutions are those that allow organizations to grow, evolve, and expand business initiatives as the pace of competition accelerates.