Choosing the right data analytics platform isn't easy. Successfully navigating the 20,000+ analytics and business intelligence solutions on the market requires a special approach.
Read on to learn how data literacy, information as a second language, and insight-driven analytics take digital strategy to a new level.
People-process-technology speak is no longer enough to traverse the technological landscape. C-level executives and professionals alike must learn to speak a new language - data. This critical capability propels organizations forward in today's digital-first era.
The benefit of speaking data, a.k.a. Information as a Second Language (ISL), lies entirely in the value of achieving business outcomes through analytics and business intelligence (BI). ISL is also the foundation for the process of transforming data into wisdom and successful master data management.
Fear of disruption and growing digital transformation initiatives have created a demand for business-driven analytics. Gone are the days of centralized, IT-driven, BI capabilities (gasp!). But the reasoning is sound.
Modern era business stakeholders need current and relevant insights from across business units and processes. Traditional data sources like end-of-month statements and quarterly reports are no longer enough. Access to enterprise-wide information fuels data analytics platforms and enable a new approach for decision making.
Well, almost. Selecting the best BI / data analytics platform for your budget and organization doesn’t guarantee positive business outcomes. This is because the very best results are only achieved when core business actions are supported by clusters of analytics outcomes.
And what are these clusters of outcomes? They're the insights needed for better decision making, and they start with the business, not with the data. It's not about the technology - or solving the data silo problem. It's all about the approach.
So, how can we bridge the gap between positive business outcome and the technology required to get there? Increasing data literacy is the answer.
You can probably imagine that the top roadblock was culture challenges to accept change. And that’s a topic for another time!
Data literacy is the most crucial factor for successfully aligning your organization’s most important business outcomes with the most effective data analytics platforms or BI solutions.
The reason data literacy plays such an important role in choosing the right technology solutions is that it directly impacts the quality of the requirements list. And a better, more accurate requirements list always maximizes cost-savings and overall effectiveness of a solution.
Incidentally, this topic came up a couple of times in this conversation with a data scientist.
Data literacy is solved by a structured program of learning information as a second language (ISL). ISL eliminates data literacy by modeling the way we learn spoken language.
While there are paid and free resources that address data literacy, the best approach is to incorporate your organization's culture and existing technical vocabulary with off-the-shelf resources. You can read more on the basics of creating an ISL program here.
The most important thing to understand is that ISL is a complete system of learning, not just a list of generic terms and definitions. It includes the reports, charts, dashboards, and terminology unique to your organization.
Because business stakeholders have the best understanding of what success needs to look like, they’re accountable for communicating the requirements for how to get there.
Knowing the right business questions to ask is only half of the equation.
Choosing the best BI and data analytics platform for solving business problems requires non-technical workers to “speak data.”
A baseline understanding of data enables the proper communication required to “be on the same page” with data scientists and engineers.
Translating desired business outcomes into the language of information enables a much deeper command over both the technology selection process and the technology itself.
ISL is a crucial factor to empower digital transformation and decrease the threat of disruption.
Applied Analytics = Building a business analytics portfolio of actionable insights from a data analytics platform that will directly affect and improve business processes.
If you could know anything about a process that you think would be helpful to achieve the desired outcome, what would that information be?
Imagine looking in-between the current data points you in data analytics platform and discovering what's happening there.
You would benefit from finding out how customers feel about you on social media. You'd probably like to know if your customers are generally happy with your product or if they are shopping around. Or, if your customer service reps are doing a good job empathizing with customers. The list of insights that fill in traditional process gaps is endless.
By starting with the insights and determining where in a business process you need them, you ensure the success of both the analytics project and the business process.
By taking the organization as a whole into consideration, analytics projects take on a whole new level of value and complexity. There are solutions for unifying data across data silos, but the more information that is made easy to consume, the greater the benefit.
Examples:
These pioneers are disrupting long-seated competitors and driving massive industry change. This is especially true in financial services and with technology and service providers.
The vocabulary of applied analytics includes words and concepts such as:
Business Analytics = Focus on practical requirements needed for understanding current performance and for predicting future outcomes.
It's essentially a method for ongoing fact-based decision-making by end-users – modern business problem solving.
These requirements include fluency in:
Data is crucial to the success of business analytics. Just as Henry Ford used data to ensure success in the early 1900’s, we also depend on volumes of high-quality data.
This data is critical for answering questions such as:
If you’re interested in making applications more data-centric, check out the principles of the data-centric manifesto.
Because business analytics is focused on both current and future outcomes, real-time access to insights from data is now a requirement. So, not only do we have a new list of data sciences technology to understand, we also have new data infrastructure needs as well.
The success of business analytics lies in a few core objectives:
Machine learning (ML) centers around the learning process of computers. When we refer to ML in data analytics platforms, we are mainly talking about the process of automating and improving a machine’s ability to perform a task based on what it has learned.
In the realm of artificial intelligence, ML is king. And at the heart of ML is data science.
Data Science = a field of study which involves scientific methods, programming skills, math (calculus, algebra, and statistics), and algorithms to transform data into knowledge.
To work with ML, sample data is used to train software to discover patterns or outcomes in very large data sets. The best use-cases for ML involve data that humans would have a difficult time working with.
Recognizing text and certain features of a document require training as well. Although the challenges with text are different, the same principles apply.
Data science, combined with ML technologies in data analytics platforms, gives organizations the ability to deliver relevant, timely insights to decision makers. The full potential of these technologies is only unlocked when the demands of the business and the desired outcomes drive innovation.
Although data is the foundation and lifeblood of ML and data science, creating a data strategy that is focused on both data quality and business outcomes is critical.
From here on out, I’ll refer to ML and data science as just AI. Most organizations are eager to cash in on the potential value in AI. However, finding realistic use-cases, and building a solid road map are major barriers to adoption.
The ultimate goal of AI is accelerating the delivery of data-driven insights and knowledge acquisition.
The first barrier to successful adoption of AI-powered data analytics platform is getting past the data-first mentality. As Stephen Covey so famously said, “Begin with the end in mind.” Starting with the needed business insight and outcomes paves the way to success.
Enterprise-wide data literacy is a barrier to innovation. Increasing data literacy is a massive game-changer for data-driven organizations. Becoming a digital native isn’t optional. Organizations must adopt formal ISL programs to increase the digital fluency of workers at every level.
Start your ISL learning program and search for a data analytics platform off in the right direction. Focus on the three pools of knowledge: applied analytics, business analytics, and machine learning and data science. Streamline and strengthen core operations by achieving insight-driven analytics projects.
References:
Davenport, Thomas H.; Harris, Jeanne G. (2007). Competing on analytics: the new science of winning. Boston, Mass.: Harvard Business School Press. ISBN 978-1-4221-0332-6