In today’s market, data analytics play a large role in the success of an organization. Analyzing data leads organizations to uncover insights to make more intuitive business decisions. In the age of digital transformation, utilizing analytics within your business is a necessity to stay competitive. In this Ask the Expert interview, we sat down with our newly hired data analytics experts, Debbie Callahan and Todd Truesdell, to answer frequently asked questions surrounding data.
What does it mean to be a data-driven organization?
Data-driven companies are leveraging the vast amount of data available in and outside of their organization and applying analytics to generate meaningful insights that can be leveraged to make decisions for optimal business outcomes.
Intuition and experience alone can no longer be the strategy for making business decisions. The pace of change in customer expectations, availability of data, and technology advancements are allowing business users to quickly make fact-based decisions and respond near real-time to trends, as they surface.
Data-driven organizations deliver agility, flexible architecture, and trusted sources. These capabilities reduce costs by eliminating siloed systems and ad hoc solutions. The efficiency gains provide more time for the analysis of data, resulting in new insights and better business decision.
How have analytics changed?
Over the last three decades, insights have evolved from a rearview mirror analysis of “what happened” to today’s mainstream capability of “predicting what will happen”.
There are three drivers accelerating the future of analytics and the need for digital transformation:
- The pace in consumer behavior is changing; Gartner estimates that by 2020 the customer will manage 85% of relationships with no human interactions.
- The volume, velocity, and the variety of data available to the enterprise. Forbes states that more data has been created in the past two years than the entire previous history of the human race. It is also estimated that less than 0.5% of all data is ever analyzed and used (Forbes).
- Advances in technology – the vast number of cost-effective options to store, compute/manipulate, and manage large data sets, have opened up new insights that were not feasible in the past.
The push for digital transformation has become a board-level priority. The transformation is challenging CEO’s to rethink their business model, the way they are organized, and how they execute. The Chief Data Officer and Chief Analytics Officer are two executive roles introduced over the last several years to assist organizations in leveraging their data assets as a competitive advantage, to drive innovation, and improve the experience and understanding of customers.
These executives no longer need to fear the cost of having too much data. There are untapped opportunities in leveraging the 90% of enterprise data that is unstructured. The cloud and advances of new technologies are being leveraged to analyze large datasets faster and cheaper, ultimately delivering meaningful insights.
How does machine learning fit into analytics?
Machine learning is a method of data analysis that automates analytical model building. It applies mathematical and statistical modeling to predict and automate process. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. By utilizing machine learning, businesses can optimize and uncover new statistical patterns, which analysts cannot do with SQL and reports.
There are many pre-built APIs and pre-trained models that allow users to jump start their journey. Organizations are finding an unlimited number of use cases leveraging image analysis, speech recognition, natural language, and geospatial components.
How are companies using IoT to enhance analytics?
The Internet of Things (IoT) refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between these objects and other Internet-enabled devices and systems. While Big Data is large amounts of human-generated data, IOT is a high volume of machine-generated data.
Sensor data like other sources of data requires cleansing, securing, governing, and analyzing. But there are a few steps to take into consideration. When collecting data from a big data system, there may be errors created from human data entry. But in the physical world, sensors can provide inconsistent data so it is vital for integration teams to have strong math skills to interpret the data.
Companies in all industries benefit from improved insights delivered from IoT, cloud, and machine learning. Data collection is done in real-time, eliminating the tedious data collection process, resulting in faster analysis of the data. IoT is improving manufacturing efficiency, streamlining logistics processes, delivering new ways for customers engagement, and in some cases, automating the decision-making process.
How does Maven help companies leverage technology advances to drive new revenue opportunities, cost savings, and customer engagement.
Similar to remodeling a home, it is great to have multiple architectural features, design functions, and material options, but all the choices can be daunting. Maven Wave’s Digital Innovation Lifecycle framework guides the development of a use case driven roadmap to assist the architecting, designing, and deployment of your solutions.
As products become more digital, analytical, and social the multidisciplinary skills required for creating them will increasingly run contrary to the way corporations organize, manage work, and solve problems. We believe that multidisciplinary teams are required to deliver both insights and adoption. Our industry-based and cloud-powered solutions integrate strategy and change management; agility and software engineering principles; user experience and design; and quantitative insights to drive innovation, agility, and speed out of organizations.
Big data leads businesses to discover valuable analytics about their organization. Capturing this data outputs analytics to help companies make smarter business decisions that can ultimately cut costs and lead to faster and smarter decision making. Big data is a key component in the digital age, allowing companies to hold a competitive edge. Contact us here to learn more about how you can incorporate big data into your business strategy.
Meet the Experts
Todd Truesdell is a Managing Director with over 20 years of data and analytics leadership and technical experience. At Maven Wave, he is responsible for data and analytics strategy, solution definition, and delivery management. Prior to Maven Wave, he held leadership positions at Clarity Solution Group, Pivotal Software, Knightsbridge, and PwC focused on value driven data and analytics solutions. Todd has led and successfully delivered several large-scale, enterprise data and analytic solutions for a variety of clients. In addition to his consulting background, Todd has held data and analytic leadership positions for GE and GMAC.
Debbie Callahan is a Managing Director with over 25 years of data and analytics consultative business development experience. At Maven Wave, she is responsible for helping our clients leverage analytics to accelerate innovations and deliver better business outcomes. Prior she worked for Clarity Solution Group, and Knightsbridge Solutions (acquired by Hewlett Packard) exclusively focused on data and analytic solutions. Callahan is recognized for her client-focused philosophy; persistence in meeting clients’ unique organizational challenges; commitment to excellence; and a partnering model that ensures ease of doing business. She has a Master of Science Degree in Management and Organizational Behavior from Benedictine University and a Bachelor of Science Degree in Marketing from Illinois State University.
Contact us here for more information.