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Gartner Says Traditional Data and Analytics Strategies Cannot Satisfy Digital Business Demands

Today, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value, according to Gartner, Inc. That is because traditional data and analytics strategies overlook the demands of digital business.

Modern data and analytics serve a broader enterprise purpose and are more integrated into the ways people work than ever before, causing a new approach to data and analytics strategy to emerge.

“IT leaders need to look at data first to succeed in their digital initiatives, rather than treating them as an afterthought to help with ad hoc projects,” said Mike Rollings, research vice president at Gartner. “Chief data officers (CDOs) must shift toward using data and analytics capabilities to transform business models and improve customer experiences, elevating data and analytics strategy to the enterprise level.”

Gartner analysts presented the ways in which data and analytics are becoming a primary driver of value generation and business strategy during Gartner IT Symposium/Xpo in Orlando last week.

Evolution of Data and Analytics

“For decades, data and analytics strategies have been service-centered, which is where strategy looked more like a list of projects or changes going on in the enterprise that required peripheral support from the data and analytics department,” said Mr. Rollings. “Today, we must inspire the use of data and analytics in everything we do and across our business ecosystems, helping our enterprise examine how the use of internal and external data can reinvent how we deliver value.”

This is important to keep in mind since defining data and analytics strategy is identified as the top responsibility of 86% of data and analytics leaders, up from 64% in 2016, according to Gartner.

Modern Approach to Data and Analytics

According to Gartner, nearly half of IT leaders plan on increasing their analytics investments to support digital transformation in the coming year. “It’s impossible to be a digital business without being a data-driven enterprise. And, increasingly, data and analytics is part of every business discussion about digital transformation,” said Mr. Rollings.

The implementation of a data and analytics strategy was ranked the No. 3 most-critical success factor among CDO respondents in the most recent Gartner CDO Survey, yet not enough CDOs have achieved a balanced data and analytics operating model.

“To be successful in the long run, CDOs need to strike a balance between tactical and strategic activities,” said Mr. Rollings. “At the heart of data-driven thinking is answering the fundamental question of ‘How can data and analytics be used to change what we do and add value?’ There is no more room for a strategy specific to the data and analytics team, but instead an imperative for an enterprise level operating model.”

The Gartner Data and Analytics Strategy and Operating Model Framework examines the required capabilities and existing deficits that must be remedied to create value inside of the enterprise. “Enterprise strategy and operating model are intimately related,” said Mr. Rollings. “There are multiple feedback loops in play, and a change in one likely impacts choices in others — especially in volatile times. It’s essential that CDOs consider one with the other to collectively transition data and analytics from a service center to enterprise competency.”

Gartner clients can read more in “How to Craft a Modern, Actionable Data and Analytics Strategy That Delivers Business Outcomes.” More information on how to use data & analytics for competitive advantage can be found on the Gartner Data & Analytics Insight Hub

Additional analysis on driving business performance will be presented during Gartner IT Symposium/Xpo 2019, the world’s most important gathering of CIOs and other IT executives. IT executives rely on these events to gain insight into how their organizations can use IT to overcome business challenges and improve operational efficiency. Follow news and updates from the events on Twitter using #GartnerSYM.

Upcoming dates and locations for Gartner IT Symposium/Xpo include:

November 3-7: Barcelona

November 11-14: Goa

November 12-14: Tokyo

March 2-4, 2020: Dubai

May 11-14, 2020: Toronto

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How Predictive Analytics Streamlines Customer Engagement in Financial Software

Global digitalization makes financial institutions (FIs) face severe competition, giving their clients plenty of opportunities at the same time. The clients, though, don’t seem to be satisfied with what they get from the majority of FIs.

An image of a digitally-connected client is now more demanding but less loyal: If FIs fail to cater to what customers demand, they will lose clients to competitors who will meet their demands.

The key to higher satisfaction and retention rates lies in predicting customer needs and delivering accordingly.

Data science and predictive analytics(link is external) help improve customer experience and create new revenue streams for financial companies.

Some digitally-minded companies, such as banks, have already succeeded through driving customer engagement in financial services. For example, Discover(link is external), an American financial services company, uses predictive analytics to reduce attrition and increase the rate of successful payments(link is external).

Financial institutions can better engage with customers by working with valuable data such as customer profile information, transactional data, and customer feedback.

8 Ways Predictive Analytics Can Improve the Customer Experience in Financial Software

Meet Customers’ Expectations and Collect Feedback
Mitigate Potential Challenges
Help Customers Feel Safe by Preventing Fraud
Recruit and Keep the Best Talent
Ensure Data Traceability
Gain a Competitive Advantage
Build Trust and Loyalty
Target Niche Clients

Data Management Provides a Solid Foundation for a Better Customer Experience

Transforming the customer experience in banking and other financial services is now an inevitable part of development strategies – Or, at least, it should be, if companies want to receive a bigger market share and drive customer engagement in financial services.

Data management includes enterprise data such as customer, market, and transaction data as well as big data such as that from social media, email, images, and web logs.

In the machine learning phase, developers capture feedback, prepare data, and then train and apply the model to applications and processes.

Collecting, managing, analyzing, and applying data to processes can help businesses improve their overall customer experience.

Predictive analytics, on the other hand, has more to do with the application of the data managed.

According to Aberdeen(link is external), predictive analytics can boost your sales through:

Communication
Statistical model
Consumer behavior
Machine learning
Patterns
Visualization

Predictive analytics in finance(link is external) can help businesses engage customers, streamline internal processes, better manage risk, provide better training for HR recruiters, and ensure data transparency.

1. Meet Customers’ Expectations and Collect Feedback

Customers expect more businesses to cater to their needs and time.

Nearly half of banking customers (40%) are willing to pay more for simpler experiences and interactions, according to EY Global Consumer Banking Survey(link is external).

In fact, most customers prefer non-bank providers because they offer a better customer experience than traditional market players.

Additionally, the number of clients planning to turn their heads and wallets towards digital-only banks increases daily.

Including machine learning algorithms(link is external) and predictive analytics techniques while shaping the tactics on how to improve customer engagement in banking is an investment for FIs to consider.

2. Mitigate Potential Challenges

Banking entities can more easily preform daily tasks through machine learning and predictive modeling.

Predicting operational demand based on historical data and future events can help predict:

Demand spikes and rush hours
The amount of cash required for specific ATM locations
The effectiveness of chatbots versus call center operators

Being able to predict situations that may cause productivity to slow can help FIs prepare in advance and make internal processes smoother.

3. Help Customers Feel Safe by Preventing Fraud

Through building predictive analytical models, banks can identify and prevent exposure states.

Stress testing, bank capital adequacy, and market liquidity risk of banks are calculated and predicted against existing standards.

With data insights based on predictive modeling, banks can prevent credit defaults through tailored collection strategies and borrowers’ segmentation. The same is with the risks of fraud.

Detecting and preventing fraudulent activity are the main tasks for data management. Tracing transaction anomalies and suspicious activities and real-time responding to threats help financial institutions fight with this pressing issue.

4. Recruit and Keep the Best Talent

In terms of human resources (HR), banks should consider how to:

Optimize of the employee count for effective operation
Motivate personnel
Reflect the company’s image in a positive light

Data management and financial data analytics can help HR departments measure the effectiveness of incentives and training performance.

It can define how many personnel each branch requires and create models for salary optimization.

5. Ensure Data Traceability

The word “regulations” often appears next to the word “financial” – and it’s no wonder this industry is exposed to a growing number of regulatory and legislative initiatives.

Banks tap on data management to ensure data traceability.

With the timeliness of reporting, data management and predictive analytics also provides adequate stress management and credit management models.

6. Gain a Competitive Advantage

In finance and banking, service or product differentiation is quite narrow. And that’s one of the reasons why the battle for customers is so severe.

Streamlining customer engagement through the financial software products can turn the battle in favor of the more tech-savvy side.

7. Build Trust and Loyalty

Streamlining customer experience is a must-to-include item while developing both marketing and digital strategies.

Delivering the right customer experience means that a business understands its clients with their needs. Only through appreciation on the client’s side, brands can build up loyalty and trust.

8. Target Niche Clients

In financial business, similar to the other domains, some clients are difficult to categorize. These people are often influencers and have a very particular mindset and social standing.

Businesses are often afraid of such people since they can be equally beneficial and destructive for the business’ image.

However, ensuring them with 5-star customer experiences can be a solution. Think of targeting them through one-of-the kind personalized propositions or customer-tailored promotions with the right messages at the right time and device.

These are the most typical areas of customer-related concerns, where predictive analytics can come into play:

Transparency in communication and fee collection
Smooth omnichannel experience
Smooth operations between digital banking channels
Personalized offers and advice on preferable financial services
Customers segmentation
Claim management

If you are thinking of achieving similar goals, it’s high time to start considering predictive analytics for FinTech and banking(link is external).

Predictive Insights Help Win Users’ Hearts

FinTech startups are rising as competitors challenging traditional banks, while financial titans are actively embracing digital transformation. Big data is a game-changer here. Only those capable of harnessing and managing it will get ahead and meet the growing expectations of clients.

Embracing predictive analytics as a part of technological strategy helps get precious customer insights, enhance cross-selling and speed up customer acquisition.

Add exceptional fraud preventing capabilities and the ability to streamline operations, and you’ll get a powerful tool in your inventory.

Even though digital clients are picky, they are predictable. Don’t take too long to learn what will make them stay with your company instead of moving to one of your competitors.