Businesses face an uphill battle when understanding and leveraging the extensive amounts of data at their fingertips. They are working through disconnected, heterogeneous data sources in an attempt to paint a holistic view of both their end user and overall business goals. There is more data available today than ever before – from across the company, through third parties and social media. Many organizations are still unable to move beyond the realm of pure data intelligence to an analytics strategy that helps them reach their business goals.
Big Data Gets Bigger
Companies are inundated with an ever-expanding pool of business insights that they just don’t know what to do with. Big data, as companies are now measuring it, goes beyond representing just high volume to also include the velocity (streaming data, IoT etc.) and the variety (text, images, videos, documents, web content) of data being recorded. Understanding how big data, in all its forms, and technology can coexist in a symbiotic relationship to solve real business problems is critical. With the emergence of PAAS (Platform as a Service), IAAS (Infrastructure as a Service), SAAS (Software as a Service), or ITAAS (IT as a Service), companies now have more options than ever to collect and efficiently store data. Yet, they still struggle when creating a framework that enables them to connect all the dots and put that data to use through meaningful, actionable insights – precisely at the point of action.
For those looking to harness the power of big data, here are five steps that will help you go from collection to action.
1. Map and Profile Data Sources and Identify Outcomes
In order to leverage the influx of data at your disposal, you must first have an understanding of your business processes: know where your data is derived from and assess the quality of data, workflows and methodologies. Big data implementation comes with challenges around volume, complexity, variety and speed; understanding your business from the ground up can help you in defining an effective data strategy to address or even prevent these issues.
You will also need to consider how a new data strategy will impact your top and/or bottom line when implemented. Think about the use cases tied to your business goals – i.e. entering new markets, streamlining operational performance, bringing innovation to transform processes etc. – and envision how improved data management will allow you to get there. Once you audit your processes and walk through the desired outcomes, you’ll be well equipped to define the metrics, develop a roadmap and determine the required investment to achieve your end goal.
2. Start Small, then Scale
Developing a simple, yet scalable plan for bringing data sources, analytics tools and talent together will enable you to mobilize management and staff support, while avoiding large setup capital. Select one use case or business problem – one that would benefit from big data analytics – and then build upon it once the business value has been validated. There are existing tools and resources that can help, including modern cloud platforms, such as Microsoft Azure, Google Cloud and AWS that work to eliminate lead-time and cost barriers by accelerating hypothesis outcomes. Once you have your initial big data process setup, ready to run and delivering the desired results, you can always scale further and expand your big data universe.
3. Select a Big Data Implementation Partner
According to a recent IDC report, worldwide revenues for big data and business analytics (BDA) solutions are forecast to reach $189.1 billion this year, an increase of 12% over 2018. By 2022, IDC expects worldwide BDA revenue will be $274.3 billion.
In this environment, finding the right technology skills or big data implementation partner can be a daunting task. The big data landscape is rapidly evolving with new and existing players providing innovative solutions. So where do you begin to identify the best match for your business goals? When vetting a potential software solution or implementation partner, there are several factors to consider. Consider the value they can provide in terms of:
- Technology Stack: Cloud, Database, Infrastructure, System Integration, Extraction/Loading, Testing, Governance, API/Connector Development.
- Data Talent: Developers, Data Engineers, Data Scientists and Analysts.
- Problem Solving Competencies: Data Science Labs, AI tools, Consulting Skills.
- Domain Experience: Success Stories in different industry verticals, Frameworks, References.
- Partnership Ecosystem: Infrastructure, Platforms, Applications, Technology.
- Engagement Models: Outcome-based, Strategic, Hybrid.
- Support: SLA-based service desk.
Identifying a thorough list of selection criteria will allow you to choose a partner that is best suited to meet your big data needs based on the ROI and overall impact they will provide.
4. Implement a Cohesive, Scalable Data Management and Governance Strategy
Nobody likes dirty data; it can bog down a system, cause harm and generate inconsistent results. When implementing big data, it’s imperative to enact a data cleansing process to ensure that outcomes are consistent, accurate and reliable. As organizations collect more data, and more varieties of it, they are faced with a double-edged sword in that, while the gigantic amount of data is advantageous, it also requires companies to implement an efficient process that addresses the ever-changing landscape of technology and data evolution. So the key to successful data management is creating an agile, exploratory ecosystem for big data applications. In other words, an ecosystem where the results of computations evolve in accordance with the detection and extraction of more signals. By implementing a cohesive, scalable data management and governance program, you can coordinate diverse data disciplines and overcome the challenges associated with data siloed and saved across the organization. Then, you can easily identify data sources and monitor data interaction between all applications.
5. Evaluate Results and Share Success
After implementing a big data strategy, it’s essential to share your learnings with your team, managers and company. Demonstrating how these insights have directly impacted leadership’s ability to make critical business decisions will help you build the case for a data-driven approach. Start by providing a measurement for aspects of the business thought to be immeasurable before – such as predicting bottom-line results with ROI, understanding customer behavior and action to personalize offerings, or improving R&D by analyzing feedback data in real-time, among many possible aspects relevant to your business.
Combining big data with the right tools can help businesses derive critical insights. The key is to think strategically and put the right processes and foundation in place to drive a sustainable analytics strategy. Data for the sake of data is a losing formula. Winning organizations make the most of their data sets with an analytics program that can assess any and all bytes of the business.