Three Ways to Fast-Track Your
Obtaining business value from enormous amounts of data and information is a priority in today’s business. Discovering the right information can provide critical competitive advantages by exposing insights and opportunities that enable you to lower costs, optimize performance, improve efficiencies, manage risk, enhance customer experiences and uncover new market opportunities.
According to John A. Deighton, the Baker Foundation Professor of Business Administration at Harvard Business School, “Used well, it (data) changes the basis of competition in industry after industry.”
With every organization putting priority on instituting a big data strategy, it’s become increasingly important to not only establish a strategy, but to do so quickly and to gain early measured success. Not doing so can lead to your organization being passed up by competitors that have, along with losing revenue and market share.
Ensuring big data readiness can seem complex and overwhelming, especially if you’re not fully aware of your internal big data capabilities. Understanding where you need to invest, where your data asset value gaps are and how to measure your current big data assets, are just a few formidable tasks that may be standing in the way of maximizing your data’s business value.
Here are three ways that can help you fast-track your big data strategy and help you gain an understanding of what it takes to make your strategy successful:
Understand the Business of Your Big Data Strategy
Understanding your organization’s main business initiatives is vital to successfully identifying the data, supporting decisions, use cases and analytics, along with the fundamental architecture and technology requirements you need to accelerate your big data strategy.
Simply put, you must understand what’s important to your organization, why it’s important and what the desired business outcomes are, before you can transform your business through a big data strategy, or before that strategy can be declared a success.
Oftentimes organizations make big data an IT project, rather than a strategic business initiative that takes advantage of the power of data and analytics to drive their business models. Unifying business and IT perspectives ensures that both are positioned as leading voices of an organization and allows for a business-led, technology-enabled approach for internal operations and partner/vendor collaborations. If comprehensive insight and organizational benefits are to be gained, the business strategy itself must incorporate big data. In order for that to occur effectively, a few tasks should be considered and completed:
Identify Your Supporting Use Cases
Not only are you looking at the areas of your business that could highly benefit from a big data initiative, you also want to identify those areas where the value may not be as high, or where there are potential implementation risks.
While business potential and implementation risks are key factors in a use case’s viability, each case should also be tied back to your organization’s financial goals and assessed against their financial impact. This can provide you with a Return on Investment (ROI) estimate that helps you determine which areas of your business would be most impactful to start your big data journey.
By prioritizing your most effective use cases and focusing your big data efforts in their direction, you stand a much better chance at receiving a quick ROI and a successful strategy launch.
Gain Executive Buy-In
Executive buy-in is essential to the overall success of a big data strategy, but this is especially true when there is an importance placed on experiencing that success more quickly.
The more engaged and connected your C-suite is with your big data strategy, the better your chance for success. They must understand what a big data adoption can do, what its benefits are, what investments are required to maximize returns and where big data will best add value to the organization’s bottom line.
But the buy-in can’t come from just the CIO or CDO. To ensure success in adoption or acceleration of your big data strategy, it’s important to gain active sponsorship from all C-suite leaders, who must align organizational assets, investments and plans with your strategy.
Prioritize Cultural Adoption
Those C-suite executives and senior stakeholders must also make it a priority to drive cultural adoption of a big data strategy across your organization. Not doing so can lead to organizational change resistance and if perpetuated, an eventual failed big data initiative.
Organizational alignment of vision and guidance on how to effectively leverage big data ensures that different groups within the organization view data-related capabilities with consistency, which reduces operational cost, optimizes performance and increases the chance for early strategy success.
It falls to executive leadership to not only execute changes to organizational culture, but also to reinforce the strategy and to remain aware of and remove barriers, hurdles, obstacles and blockers to drive a rapid, successful transformation and increase the chance for a quick ROI.
One of the easiest ways to reinforce organizational devotion and support of a big data strategy is to regularly communicate wins and recognize strategy champions for the efforts. On the other hand, a lack of communication can actually slow or delay progress by reinforcing that the business-as-usual of the past is satisfactory.
Another way is to accurately define key metrics and success criteria and make them well known across the organization. This also helps minimize confusion or misinterpretation by reinforcing the consistency of how data initiatives are measured, evaluated and tracked.
Taking Full Advantage
By understanding your organization’s main business initiatives, you can utilize a big data strategy to identify valuable business insights that can help improve the outcomes of those initiatives by:
- Accelerating and understanding business innovation
- Transforming business processes
- Proving out your use cases
- Providing advanced visualization of your business data
- Identifying new revenue opportunities through data analysis
Choose the Right Technologies
It’s likely that your existing architecture is made up of both modern and legacy systems that have come together as a result of in-the-moment upgrades and extensions that provided a solution for what was needed at the time, without consideration for an overall business strategy. It’s also likely that a fair percentage of your systems were never designed with big data in mind.
Some of your legacy technology may also be providing limited business value in relation to cost, performance or quality needs. It may also be holding back the adoption of more innovative technology or more efficient business practices. While the planning and eventual alteration of legacy technologies may be complex, in order to increase the chance of early strategy success, it’s crucial to accurately assess the state of your IT infrastructure and recognize where there are opportunities for improvement. There may be technologies available that can better support your growing and evolving business needs, given the explosion in data and data sources such as video, Internet of Things (IoT), sensor technology and more unstructured data.
Implementing better-suited big data technologies can help you achieve strategy success more quickly by helping you to:
- Unlock value in new and old data sources
- Improve risk management
- Gain enhanced insight by correlating data over disparate systems and data silos
- Improve business decision-making by delivering real-time data insight
- Enhance security access controls
- Expand revenue streams
- Improve business processes/operational efficiencies
- Quicken your innovation speed and time to market
As far as your needs go, all technologies are not created equal
Big data technologies can help make your journey more manageable and successful, while enabling you to move quickly from exploring high-level data concepts to driving business innovation and back again. But given the unique and individualistic nature of IT infrastructure needs, the “right” technologies can mean different things to seemingly similar organizations.
Determining what the right technologies are is a matter of coordinating needs with key factors such as organizational size and business initiatives. But there are many options available and a cookie-cutter approach would not produce the desired results, at least, not to the scale that it should.
While some legacy technologies may not be well-suited for a big data strategy, others may be just fine. Developing good reference architecture allows for new technologies and innovations to be integrated into a hybrid model with applicable legacy technologies that supports your organization as it evolves and grows. Here are a few elements to consider:
- Design patterns:
High-level models for common and repetitive architecture, such as data ingestion, storage, harmonization and access.
- Design principals:
Technical objectives and direction that helps to ensure uniformity across the areas that the big data strategy influences.
- Tool map:
Listing of tools, along with their capabilities, preferences and primary-fit evaluations.
- Tool rationalizations:
Direction for when tools should be used, with supporting justifications, viewpoints and explanations of how various tools should be used in conjunction with others and with assorted design patterns.
Are you ready for Artificial Intelligence?
Utilizing artificial intelligence (AI) and machine learning technologies can most certainly help fast-track your big data strategy, but your organization must first be ready to adopt it.
While AI and machine learning offer nearly unlimited potential, challenges such as cost, current infrastructure readiness, privacy, security and regulatory compliance await any organization ready to explore potential implementation.
Implement a Robust Governance Framework
Organizations need the ability to control, monitor and audit their data, and its use, to ensure appropriate liability management. While the content may evolve over time, the structures, controls and responsibilities should be defined and established as early as possible. This helps to ensure that suitable responsibilities are in place as the big data strategy and data solutions mature from one evolution to the next with minimal business disruption.
Big data governance is focused on agility, exploration and discovering fresh ways to get business and IT to work together. It’s also about establishing a link between big data initiatives and processes that will drive execution in business units and IT departments.
At its most effective, a big data governance strategy will help you understand:
- Where your data is
- What data is important
- How you need to manage your data
- How you want to allow access to data, who gets that access and when
- How your data will be used
A mandatory key to success
Given the explosion in data volume, data sources, and increased user demand, the need to have an effective data governance program is more critical today than it ever has been. An excess of data and uncontrolled user access can provide full freedom for users, but can result in confusion, unnecessary repetition, inefficiencies and doubt for an organization.
If weak governance processes or functions exist, or if they are nonexistent altogether, then it’s critical to address this challenge and resolve it with support from the C-suite. Data policies, quality and standards must be defined and managed in order to support a big data strategy while also complying with regulatory requirements, privacy, security and other applicable considerations.
What should an effective governance model include?
- Data Compliance and Standards:
Procedures and other criteria that must be adhered to for regulatory reasons, along with any additional criteria your organization would adopt voluntarily.
- Change Management Strategies:
Methods and procedures by which changes across the big data strategy would be introduced, evaluated, confirmed and implemented. Also defines how deviations and exceptions to the strategy standards are identified, documented and handled.
- Workflow Guidance Procedures:
Methods for defining and managing data life cycles, including operational and support-control transitions.
- Organizational Structure:
Direction for how interactions should be identified, upheld and scaled within the scope of data-related activities, including proper skill-set definitions for applicable resources.
- Data Administration, Security and Audits:
Processes that make certain that data is accurately cataloged, is of high quality and properly safeguarded for suitable authorization by permitted users.
The Governance Balance
There is a balance between being too rigid and too flexible, which highlights the need for a strategy, a process and a well-defined data governance model, to enable an effective big data strategy.
- Too much flexibility
Results in competing versions of the truth and typically results in debates, confusion, conflict and poor productivity.
- Too much control
Results in rigid processes, excessive red tape, slowed response to the business and typically leads to an explosion of business-led IT solutions.
There are two key facets to consider when defining the governance model that is right for your organization:
- Creating the best processes around data management and data analytics
How users work with data, set up processes and procedures, arrange workloads and control integration.
- Instituting the best processes for users and teams
How new or different business needs are integrated, how processes are managed, how collaboration can be better and how business drivers evolve.
Creating a well-established and agile governance model has customarily been viewed as a major challenge, but early adoption is also a major key to accelerating the success of a big data strategy.
Take Control of Your Big Data
An effective big data strategy allows you to explore your data beyond the limitations of legacy technology and practices. It encourages data-driven business processes and helps to eliminate the uncertainty you may struggle with in addressing future business dynamics.
After considering how to leverage your data as an asset, the next step is to take a look at what big data analytics tools are available. The key is to explore as many as you can to find what works best for your organization and your unique needs.