The Arizona Technology Council (AZTC) is honored to partner with incredible local organizations to help us accomplish the many initiatives we have over the year. In 2018, AZTC had the opportunity to engage MSS Business Transformation Institute (MSS) to assist us with an organization transformation.
One of the brightest young data scientists at MSS, Lu Hao, PhD, is becoming quite an expert in the space of big data and analytics and was willing to share some tidbits from her recent articles for the AZTC blog. Let’s dig in on some exciting trends and tips from our friends at MSS:
“The combination of AI and predictive analytics on big data is the next big wave,” Lu says.
A disruption is coming in the world of data analytics, powered by increasing computing power and the rapid development of artificial intelligence and machine learning.
Lu explains the traditional model of data analytics as one featuring coding-based platforms that are transformed into visual-based, interactive, code-free platforms. Enter Augmented Analytics, “where predictive analytics are combined with the power of AI.”
With these advancements, “analytics will see enormous improvements on scale, speed, and application.”
Gartner explains that augmented analytics are not meant to remove the human aspect from the equation, but to “provide deeper information to the person designing the analytics methodology…to assist in better decision making.”
Lu explains that predictive analytics will force a valuable adaptation for Business Intelligence, meaning that it will encourage analysts and leaders to ask deeper questions, going beyond “What happened?” to “Why it happened?” or event “What could happen?”
“Predictive analytics looks into the future and provides actionable insights to directly drive business values.”
In this article, Lu shares more information on taking advantage of predictive and augmented analytics and lists three key elements for a successful data and predictive analytics workflow: Data Preparation, Data Modeling, and Sharing and Operationalizing Findings.
The Biggest Data Myth: Data Infrastructure
In her second article, Lu shares the biggest myth in modern data science: “the idea that a data and analytics-enabled business value must be accompanied or even preceded by a fully upgraded and implemented data infrastructure.”
On the contrary, she says. “Data should serve the purpose, not drive the process.” Lu says that infrastructure is not indispensable, and you can still generate significant business value from your data without having it all in one place.
She suggests shifting your thought process from focusing on what data you have available to focusing on what business issues you’re trying to solve.
“Start by thinking about your desired results,” Lu recommends. “Tie analytics tightly to your biggest value drivers and largest pain points and focus on how to use data to make better decisions.”
Lu goes on to describe the revolutionary ‘Question-to-Value’ approach to reach pragmatic solutions through data. This involves six key stages:
- Action Items
This approach is all about “tying analytics directly to outcomes, taking action, and delivering value in an agile way,” says Lu.
In her third article, Lu investigates analytics maturity and how it affects the success of capturing and evaluating data.
She explains there are three components that affect analytics maturity for an organization: Technology, Business Competency, and Culture.
Regarding technology, Lu says you should consider three elements for selecting your tech: Data Infrastructure, Data Quality, and Technical Talent.
The data infrastructure lays the foundation by bringing all data sources together into a single repository. Lu recommends investing in an infrastructure with agility, so you can quickly capture new data and make adjustments when needed.
Lu goes on to emphasize the importance of your infrastructure design with appropriate data collection and storage process in mind. You must also ensure you build proper quality assurance and control processes to ensure highest quality of data possible.
When talking about talent, Lu urges business owners to “find the talent you need for a competitive advantage against your actual competitors, not Facebook or Google.” It’s easy to get caught up on the talent you can’t afford; your next analyst could already be working in-house.
Lu cautions against implementing any data analytics strategies before having your organization’s leadership full engaged. Leaders must “not only trust data to prove/disprove their own beliefs, but are open to learn from data, regardless of their beliefs.”
You also need the right team: decision-makers who can act on the data and business analysts who have the right analytical talent but also interpersonal/business skills to bridge the gap from data to business.
Finally, an organization’s culture can play a huge role in capturing, evaluating, and ultimately acting on the data. Lu suggests focusing on the following aspects of your company structure and environment:
- Define the outcome.
- Set measurable goals.
- Role Modeling
- Change Messaging
- Provide incentives to not only the employees handling the data, but also the frontline employees using the outputs.
- Build relevant skills.
Can’t Get Enough of Lu?
We know, she’s pretty great, and has a wealth of knowledge related to data science and analytics. We are lucky to have her in Phoenix.
Have a specific question for Lu Hao? You can reach her at firstname.lastname@example.org.
Interested in Advanced Analytics for your organization? Contact MSS at 602-387-2100 or email@example.com.