The idea of data generating business value is not new.  However, the effective use of data is becoming the foundation of competition. Business has always wanted to develop insights from information in order to make better, smarter, real-time, fact-based decisions. It is this demand for profundity of knowledge that has powered the growth of big data tools and platforms.

But just what is big data? According to Wikipedia, ‘’big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software’’.

Big data was originally associated with four key concepts: volume [the quantity of generated and stored data], variety [the type and nature of the data], velocity [the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development] and veracity [the data quality and the data value].

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Its philosophy comprises unstructured, semi-structured and structured data. However, the main focus is usually on unstructured data.

Evolving technology has brought data analysis out of IT backrooms, and extended the potential of using data-driven results into every facet of an organization. However, while advances in software and hardware have facilitated the age of big data, technology is not the only consideration.

The diagram below by Guy Pearce explains it all: big data and other external data are fed into data analytics to generate reports for decision making. It’s that simple.

Big data is today transforming the world of GRC. A robust GRC culture represents how organizations govern, allocate resources and set internal control practices to regulate the actions. Big data potentially transmutes all of those areas. That’s why cultures built on big data and advanced analytics are increasingly synonymous with high-performance organizations.

The value of analytics is clear: finding insights in enormous amounts of previously untapped data. This helps management to base their decisions and strategies on facts and not dreads.

With increasing complexities, businesses need dynamic solutions, new investments and functional ideas. This can be supported by innovative technology solutions that can integrate and automate various processes and controls. Such tools analyze the risk landscape and helps management to monitor them frequently.

The effectiveness of GRC hinges on data. Being able to gather, analyze, and communicate information with the stakeholders and right format is critical. Hence, forward-thinking organizations are creating an infrastructure within the organization.

Moreover, the vast and growing volume of unstructured and structured data today provides limitless opportunities to improve risk intelligence, support compliance and augment customer relationships. The key elements of a digital GRC is shown in the diagram below.

The many benefits of big data in GRC include:

• Faster and more cost-effective transaction and fraud analysis;

•  Improved continuous monitoring capabilities;

• Visual dashboards that compile data in new, more powerful ways;

• Integration of risk management, compliance, audit, and control management with business performance;

• Forward-looking (predictive) risk identification and assessment;

• Google-like searches capability of historical data; and

• Comprehensive relationship analysis for third party vendor management.

More and more organizations are investing resources to ramp up their efforts to use big data and analytics to drive growth. Yet, many companies feel they haven’t realized the full potential of their analytic capabilities.  They feel exasperated that they aren’t doing more, faster.

To be sure, an organization can use a GRC platform leverage big data for a one stop solution for the data. That way, it is easier to understand the risks, standards and internal controls governing them and facilitate a better understanding of corporate risk profile.

Being able to pull data from several sources, and then fuse the data into actionable intelligence via graphical dashboards and reports is the key to driving operational efficiency and success. Innovative technology systems can deliver dynamic data visualizations that showcase trends and patterns in real-time which will aid executives make faster decisions.

However, the focus should not just remain on what has happened in the past and what is happening in the present, but also what it means for the future; the blending of historical insight and predictive foresight paves the way for a risk averse organization.

Big data and analytics are here to stay, and only those companies that understand the immeasurable potential of these tools, and effectively tap into various big data sources such as social media, location, multimedia, text, documents, surveillance, medical records, videos, e-commerce, emails, voice, audio transcripts, stock trades, transaction logs, geospatial data, and weblogs will be best positioned to enhance their GRC initiatives.

Leveraging the right systems, engaging the right teams, and taking a forward-looking approach to big data can help fast-track an organization’s journey to arrive at the ideal data-driven GRC culture, and push the envelope to achieve long-term success. So what’s old, is new again!

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Regardless of industry, it seems that nearly every business is asking the same question nowadays: How can artificial intelligence help us?

Google Trends data shows that searches for “machine learning” — a common application of AI that enables machines to process and learn from data inputs — have skyrocketed over the last several years. It’s clear that businesses are eager to get their hands on this technology, which promises to increase efficiency and productivity.

Of course, with all the hype around AI and machine learning comes a few key misconceptions about how it can be implemented and leveraged. We asked members of the Forbes Agency Council to help clear things up and explain some realistic, beneficial ways agencies can use AI.

1. Improve Team Efficiency 

Besides AI ad tech, which is still a developing space, using AI to help company talent get their projects done faster and better has been our greatest benefit so far. In the interim, we are able to learn more about how AI, natural language processing, machine learning and big data tools interact internally before applying externally in the wild with a good sandbox environment. – Sunny Patel, TRENDS DIGITAL – Social & Digital Advisory for Corporates

2. Enhance Customer Communications 

Chatbots leverage natural language processing, simulating a live chat experience and allowing customers to have a conversation with the brand. Infinitely more cost-effective than staffing a 24-hour customer service team, the AI programs answer customers’ questions and serve up the relevant response in a conversational way. – Keri Witman, Cleriti

3. Understand Breaking News And Its Sources

The best use of AI today is in understanding breaking news and information, and the source of information. That knowledge should be used to better understand how those key constituents engage and their preferred content formats. This enables a level of personalization and relationship-building that can be used to sell products, forge relationships and build brands. – Peter Prodromou, Racepoint Global

4. Streamline The Customer Experience 

As a brand experience company focused on live programs, we’ve used AI to create voice-activated content for audiences attending live events, conferences and tradeshows. Session content, wayfinding and FAQs can all be programmed in and activated by attendees who ask questions and get customer responses. This type of application can translate to any point-of-purchase scenario. – Chris Cavanaugh, Freeman

5. Personalize Content And Delivery 

One way that we leverage artificial intelligence is through predictive data on the consumer to personalize marketing initiatives. You can leverage data about when consumers most frequently engage with email, what content is most beneficial to them, what channels they are most likely to engage with and much more, to help personalize their unique buyer’s journey. – Elyse Flynn Meyer, Prism Global Marketing Solutions

6. Optimize Your Ad Campaigns 

Programmatic advertising is a good example of AI application. Algorithms are used to analyze online behavior so that advertising campaigns can be optimized to maximize conversions. Using AI platforms, we can learn very quickly about the best combination of copy and creative that works across Google and social media platforms. – Alannah Tsimis Sandehl, IDM Brand

7. Streamline Small, Appropriate Components Of Your Processes 

The problem I see with bots and AI is that everyone is trying to automate the entire process. Instead, I suggest taking small components and using AI to make it easier or streamline that one piece. The “out of the box” AI platforms are not one-size-fits-all solutions. – Gary Henderson, DigitalMarketing.org

8. Analyze Data Better 

When people say “AI,” they really mean machine learning. Unsurprisingly, machine learning is best used in analytics platforms at most agencies, where it can attribute value to different pieces of content, contribute to social listening, identify trends and key messages, words or phrases, which help determine how well a company’s positioning sticks. – Kathleen Lucente, Red Fan Communications

9. Predict Content Performance 

One way to leverage AI is to help predict branded content performance. Advertisers should no longer go with their gut when choosing marketing messages that’ll resonate with consumers. By merging the art and science of content creation, marketers are now able to make strategic decisions that deliver measurable results, even when it comes to something as seemingly subjective as a brand’s creative. – David Shadpour, Social Native

10. Improve Decision-Making 

Right now, artificial intelligence is best at deciphering large amounts of data sets and making pre-programmed decisions based on the results of the input. In this regard, the AI algorithms are far faster than the human brain. The best use of AI is to compile, decipher, record and execute decisions based on large amounts and a variety of metrics. – Ernesto Carrizoza, Movement Marketing

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