IT Services, Thought Leaders

How Companies Can Maximize the Value of Data

October 19, 2018

by Michael Grebennikov

Managing Partner at Digiteum

Businesses must learn to extract value from data. You can use this article to learn how to build a smart data strategy and how data visualization and analytics help companies leverage their data assets.

The volume of data is growing exponentially. According to IBM, 90% of all the data in the world has been created in the last two years, with over 2.5 billion gigabytes of data added to the total volume daily.

Companies across all industries are focusing their efforts and investment on capturing this data’s value. In fact, more than half of businesses (53%) say they adopted Big Data analytics by the end of 2017.

Today, the majority of enterprises struggle to leverage the full value of the data they own or have access to. McKinsey estimates only 1% of data coming from about 30,000 sensors at an oil grid turn into insights.

You can use this article to learn more about:

  • The obstacles businesses face when adopting data analytics.
  • Where data analytics can be applied.
  • Five steps for implementing data analytics in your business.

Why Data Is Valuable for Businesses

Despite the many challenges, enterprises of different sizes with diverse resources can leverage data successfully.

According to a 2017 Experian report, data unleashes a wide range of opportunities: improved customer service, risk assessment and mitigation, increased revenue, and more.

https://lh6.googleusercontent.com/M5sYI5HL_nLa0gopBwHTP72tIgwDyRVJb23IM-92aQ4onbblf929BOzXsalvDfS2HbPmqO2ojAQYiV8nP2kuzsnN3DEhCL38ZLP6mUyJ7dmsE2lUmr17ESIqydZRQeheUTQjmbsC

Image credit: Experian

As you can see on the chart, the majority of companies use data to increase revenue (61%) and provide better service to their customers (56%). The range of benefits is diverse and presents even more opportunities for business.

As the volume of data and interest in data analytics grows, new business cases for investing in data analytics are emerging in many industries.

4 Key Obstacles When Analyzing Data

Companies struggle to extract the full value from available data assets for 4 key reasons:

  1. Low Quality of Data: In order to trust data, decision-makers need to be sure their assets are accurate and complete. Unfortunately, raw data is quite the opposite in most cases due to human mistakes, equipment errors, fragmentation and inconsistency of data sets.
  2. Unmanageable Sources and Volume: Data is heterogeneous and varies in forms, types, and sources. Therefore, huge amounts of data remains unused because companies simply can’t handle the volume or don’t fully realize how much data they create and have access to.
  3. Lack of Data Strategy: Executives often make ad hoc attempts to leverage data and fail to elaborate data strategy that would integrate with a key digital strategy or overall business plan. Such attempts usually bring temporary results and barely address the demands of today’s data-driven economies.
  4. Insufficient Infrastructure: Not many companies have adequate infrastructure in place able to handle data projects. Instead, data-specific roles and responsibilities are randomly distributed among different departments, such as IT, marketing, customer service, sales, and finance.

Overcoming these challenges allows companies to successfully leverage their data and use it to drive profit and growth.

4 Areas Where Companies Can Successfully Leverage Data

Businesses can use data to drive profit and growth in many areas:

  1. Visualization and analytics of performance data
  2. Visualization and analytics of customer data
  3. Analytics of operations data
  4. Telemetry and monitoring for in-house systems

By studying real-life cases of successful data analytics and visualization, you can identify areas where your business can leverage data, too.

1. Performance Data Optimizes Conversions

Performance data is defined as information coming from digital tools such as mobile apps, physical products or even human-provided services.

Companies can use performance insights to:

  • Monitor and assess effectiveness and conversion
  • Identify weaknesses and strong points of a system
  • Find optimization opportunities
  • Redesign products and services

Overall, performance data is a valuable asset that helps companies analyze and improve their products, services, and systems.

Feed.fm, once a California-based startup, is now a successful technology company providing a SaaS audio platform to application developers such as Fitbit and Shazam.

In addition to providing a software development kit for companies, Feed.fm leverages media performance data, using these insights to both improve its own services and empower clients with valuable information about customer experience.

The company relies on real-time and historical data visualization to demonstrate the main metrics of media augmentation including sessions length, as well as the time and frequency of playbacks.

Advanced data analytics allows Feed.fm to correlate these metrics and determine how media augmentation influences conversion, what factors influence customer engagement, and how to improve it. Thanks to audience insights, for example, Shazam was able to improve user experience and achieve sessions that were 5 times longer.

In general, Feed.fm example demonstrates how leveraging performance data helps companies optimize their services and improve conversions.

2. Customer Data Can Provide Personalized Experiences

User data has tremendous potential to improve every customer-focused industry, from retail to manufacturing and healthcare. Thanks to social media and online services, the amount of available user data is increasing exponentially.

Leveraging user data is essential for every company that wants to keep up with the competition and provide relevant experiences tailored to their customers’ needs.

Virtualitics helps clients better understand their customers and users through data-centric tools for 3D data visualization in VR and collaborative analysis for shared virtual spaces. Additionally, Virtualitics uses advanced analytics based on machine learning (ML), defined as analytics performed by machines automatically based on statistical techniques and self-learning with data, immersive data experience.

Using an immersive digital environment, 3D data visualization, and ML-based data analytics, Virtualitics generates dozens of insights on buyer behavior within minutes and identifies correlations out of hundreds of variables known as customer characteristics.

As a result, Virtualitics’ tools allow companies to extract complex insights based on their customers’ actual behavior, while showing these correlations in a clear, easy to understand format.

Virtualitics uses a 3D visualization of customer data.

Image credit: Virtualitics

This image demonstrates 3D visualization of customer data insights in a virtual office.

Emerging tools for data visualization and analytics allow companies extract more value from their data and therefore make a bigger impact.

3. Operations Data Helps Cut Costs

By using data-rich technology and predictive analytics, companies can improve operations, increase efficiency, and reduce maintenance costs.

Global delivery company UPS pioneered the practice of using sensor-based technology to gather operations data. CTO Juan Perez shared the impressive results of UPS’s innovation strategy at IoT World this year and demonstrated how the company enabled serious cost reduction thanks to IoT and data analytics investment.

Today, all the processes at UPS are connected into the same data analytics infrastructure. Trucks, drivers’ handheld devices, bulkheads, and every package are equipped with sensors that continuously send data on what’s happening on the line.

Data analytics allows UPS to optimize drivers’ routes, monitor parcels, maximize packaging, and prevent and reduce breakdowns or losses. In the end, this data-centric initiative brings UPS $50 million in savings every year and is expected to deliver even better results in the future.

This approach can be applied to the operations in different fields, from complex manufacturing at the factories to managing energy consumption at the office or inventory in the supply chain.

Investing in data-rich connected systems allows businesses to reap viable financial returns.

4. Telemetry Monitors In-House Digital Systems

Telemetry allows companies to measure performance and conditions. Additionally, telemetry enables companies to retrieve valuable data from devices and basically any access point remotely. More companies depend on telemetry to monitor different systems, from power consumption to IT system health.

Companies store and manage assets including customer data, accounting reports, operation records, and transaction history. Relatively few, however, proactively think about security and maintenance.

Any malfunction in an IT system can lead to undesirable results, such as loss, theft, or modification of data.

Applixure, a Finnish data analytics platform, helps businesses keep their IT systems healthy. Applixure’s platform collects performance data from hardware and software, providing visualizations of the whole PC environment to provide real-time analytics of what’s happening within the given IT system.

Once Applixure indicates any malfunction or overload, it immediately provides warnings to prevent breakdowns and fallouts.

https://lh4.googleusercontent.com/fce1gq0g9z2QDiKCHAum8D_l5VnDSc6vBBY3iGy3JYxGcN1OTpT9x4uWdWiK6ngV6VndJLyPnTZCSZGATucE-mxzxIhoMS6w83WV4b7bTv4gb7JLVDxUzsWoUPTHemSYNSgP-eD-

Image credit: Applixure

This image illustrates an Applixure dashboard showing the basic parameters of software monitoring.

Companies can use data analytics to preserve their IT system health, cybersecurity, and sustainability. Companies can also reduce maintenance costs and improve efficiency by using data insights from in-house digital infrastructure.

5 Steps for Building a Data-Driven Business

Businesses can improve their ability to collect and analyze data in 5 steps:

  1. Create data infrastructure
  2. Define data strategy
  3. Prepare data
  4. Visualize data
  5. Analyze data

Step 1: Create Data Infrastructure

Building data infrastructure includes a set of initiatives, such as assigning data ownership, roles and leadership able to manage corporate data.

Additionally, data infrastructure requires setting data-related processes and adopting relevant technology for data security, monitoring, visualization and analysis. As a result, the quality of data infrastructure determines the maturity of data management in the company.

This image demonstrates how the development of data infrastructure impacts the maturity of data management in the company.

https://lh4.googleusercontent.com/NlS7YRC03vb7IvPiYS1vpK4AG57rInmUf_gwlhwLlxzG56N45NpF6Ria8COIDazPz8fFnJbJIK0I9Y_PGxXR0rpjsJ7G4PoIJi0qbe9mPxScVvHYC8uIn2I3jZ5wDrEmKRQVoy1u

Image credit: Experian

The graph shows the path from inactive to optimized enterprise and how improving data infrastructure impacts the trust in data assets within the company.

Therefore, setting data infrastructure is an important first step that builds the foundation for further data initiative.

Step 2: Define Data Strategy

A company can define its data strategy when it has data infrastructure – responsible leadership and data professionals able to create a tactical approach. At the next step, data executives and decision-makers assess a company’s potential, identify goals, analyze market, competition, technology, and available data assets and resources.

For example, the previously mentioned UPS initiative identified its goals and put improving efficiency and cutting cost in the center of its data initiative. It determined further digital transformation that brought the company $400 million in savings per year.

With a data strategy in hand, executives can further prioritize next efforts and build a plan.

Step 3: Prepare Data

Data from different resources won’t bring much value until it’s properly vetted and cleaned. The next step is to figure out the tools and processes needed to collect or create credible data sets, cleanse this data for further processing, and choose a reliable cloud-based storage.

Applixure, for example, deals with large volumes of data coming from different devices, software, and other touch points. Before it sends data to relevant dashboards, the system collects data from different sources, filters it, and further processes it according to user needs.

This step is important because it defines the quality of data at the output.

Step 4: Visualize Data

Data visualization can be a benefit on its own. At this step, data leadership determines how to configure data visualization tools, what parameters and variables to demonstrate and how to correlate different data streams to extract the best of value from given sets.

Virtualitics, for example, provides a wide range of ways to demonstrate data and allows its clients choose between hundreds of correlations depending on their goals in the immersive VR environment.

At this step, data already becomes visible and therefore brings the first value.

Step 5: Analyze Data

Analytics help businesses derive insights from data. This is the ultimate goal of the whole data initiative, because data insights allow executives to make data-driven decisions.

At this step, data leadership identifies the processes and tools for data analytics; decides whether to involve high-end technology such as machine learning; combines the efforts of data scientists and machine algorithms; or fully relies on existing data analytics tools or technology partners.

Demonstrating sessions length and the changing number of listeners could be relevant for Feed.fm clients, because it already provides the statistics on how the customers use the system. However, using advanced analytics reveals brand new opportunities and allowed Feed.fm clients to see what influences conversion and how to increase it.

The final step is important for the companies who want to uncover the deep impact of data on their business and take data-driven actions rather than simply monitor operations and processes.

Data Analytics Present Challenges and Rewards

Successful business cases and billions of saved dollars only prove that companies will continue investing in data technology.

However, real-life examples also show that transition to a data-driven environment presents challenges. Apart from dealing with a number of obstacles such as unmanageable data volume and low quality, companies have to go through a long journey to extract value from their assets.

Businesses that invest in data analytics, however, eventually reap the benefits. Companies learn how to make better decisions based on facts, strengthen their competitive advantage and gain new ones, optimize their processes, products and services using real data, reduce costs and sometimes even turn data into a profit center.

Overall, data will play an important part in the way businesses and organizations develop and make decisions today and in the future.

 


About the Author

Headshot of Michael GrebennikovMichael is a co-founder and managing partner at Digiteum technology company. Together with Digiteum team, Michael helps businesses grow by leveraging digital technology and introducing innovation to their operations. Relying on his 20-year expertise in IT, Michael shares his knowledge and experience on an array of topics, including digital transformation, strategic planning, innovative technologies, and business growth. 

 

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