Two Hot Trends for 2021
Happy New Year! So what’s new for 2021? Everyone has an opinion about 2021’s hot trends for analytics and BI. I have my own, and I think the analytics marketplace is poised for an explosion of new technologies and products, but I think there will be two trends that transform the analytics/BI landscape in 2021:
- Machine Learning to Augment self-service analytics
- Data governance makes a comeback
Machine Learning to Augment Self-Service Analytics
As more sophisticated, and easy-to-use, analytics tools have hit the market in the past five years, self-service analytics has become a must-have for just about any company in any industry. To be sure, self-service capabilities take some of the pressure off IT staffs to produce, and they’ve created a new generated of citizen data scientists, but that’s a double-edged sword.
As data volumes become larger and more complex, citizen data-scientists are often overwhelmed by the process. Increasingly, decisions require cross-functional knowledge, and the number of variables that drive decision making and actionable insights is multiplied almost exponentially. As a result, business people acting as analysts often fall back on their biases and only explore the hypotheses they started with. Thus, they’re likely to miss key insights that may go against their biases or hypotheses. When that happens, decision-making is hampered by incomplete or faulty information, and the analytics effort suffers.
Business people acting as analysts often fall back on their biases and only explore the hypotheses they started with.
Enter AI-specifically, machine learning. Machine learning algorithms will serve to augment analysts’ knowledge and enrich their understanding of their decision-making environment-spanning departments and functions. This will take place across the analytics continuum from data preparation, to analysis, to insight, to action.
Augmented analytics and data preparation will enable “Smart” data discovery that will eliminate, or greatly reduce, bias in decision-making via an impartial view of the data and the results produced from the analysis of it. The result? A virtuous cycle of increased trust in the analytics system, which leads to high user adoption rates (more people making better decisions). This leads to increased ROI via better decision making and the ability to act more quickly on those decisions.
AI/ML Powered Analytics Players
Model Op : Providing Analytic Ops via ML/AI
narrative science : AI driven data storytelling
Alteryx : Data science and AI without coding.
Data Governance Makes a Comeback
A client once complained to me that he was, “Drowning in data but dying for information.” That was almost 20 years ago, and it’s only gotten worse as data has increased in volume, velocity, and variety. The flood of data that inundates most companies on a daily basis is enough to fill airplane hangar’s worth of old mainframe computers. What’s more, the majority of data these days is unstructured. How do you make sense of all that data? How do you standardize it and make it usable? That’s where good Data Governance (DG)-and the necessity for a Chief Data Officer (CDO)-comes in.
Some of my colleagues make a good argument that there’s not room in the C-suite for one more person-especially one whose only concern is data. Maybe that was true five years ago, but not today. CIO’s have their plates full with organizational strategy, data security, and overall analytics implementation. Data governance work is mired in the muck of technology and politics, and with the volume and types of data that need to be governed, the process of DG needs a dedicated leader.
Data governance is primarily concerned with making sure that data is consolidated, standardized and governed across the enterprise. It’s also process-oriented. One of its critical mandates is overcoming cultural and territorial boundaries to put policies and procedures in place that ensure that the organizational data, no matter the system it resides in, is high-quality and provides a “single version of the truth” to anyone who accesses it. For that to happen, someone needs to take accountability for the impact that good-or bad-data has on the organization.
It is critical to overcome cultural and territorial boundaries to ensure that the data provides a single version of the truth, no matter the system.
That’s where the CDO comes in. Under a CDO, the DG function can garner the visibility, budget, and resources to meet its mandate. Additionally, a CDO can provide the strategic guidance to the DG process to steer the effort through choppy political waters that de-centralized DG efforts cannot. So, yes, it’s always a good idea to think before you split executive responsibilities and add yet another direct report to the CEO, but in this case, because data is such a valuable strategic, yet complex, asset, the CDO position has become mandatory.
To be sure, there are other analytics trends that will emerge in 2021, but I think these two-because they impact the adoption and control of data-will underpin most of them in one way or another. It’s going to be an interesting year.
I’d love to hear what you think about the hot trends for 2021. You can message me on Linkedin, or email me at anu@nexuscognitive.com
