5 Steps to Harness Big Data for Powerful Insights

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Shweta Joshi

August 14, 2018

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5 Steps to Harness Big Data for Powerful Insights

Big data is now a mainstream concept (almost), comprising vast amount of diverse data forms from computers, mobile devices, machine sensors like biometric devices and more. Leveraging this data via big data analytical tools and platforms can help your organization gather, store, manage, maintain and analyze this data to derive powerful insights for informed decision-making and competitive advantage. But to utilize big data effectively, the organizations should have a pre-defined strategy for data engineering, advanced analytics and data visualization.

According to a recent survey published on HBR, the top objectives for big data initiatives for fortune 1000 executives include decreasing expenses, finding opportunities for innovation, new product launches, adding avenues for revenue growth, internal optimization and more.

Top objectives for big data initiatives

Organizations are generally good at collecting data from multiple sources, but in most cases, there’s a huge scope of improvement in the strategy, processing and analyzing of this data to derive true value. They are flooded with data but have been unable to harness that data via big data tools to solve business challenges or support growth goals. Some of the primary reasons for this include having an extremely complex data culture to navigate and manage, a shortage of resources, improper tools, lack of a strategy and/or improper guidance etc.

To assure value is derived from your big data initiatives, focus on the following 5 steps:

  1. Identify crucial business challengesIn our daily professional lives, challenges are commonplace – be it with processes, systems, strategies, customer experience, product innovation, competitive preparedness, etc. These can be found within departments, business units or company-wide. The key in getting the most value out of your big data initiatives is to identify and document the business challenges that most impact the business – in ROI, productivity, revenue, etc. Uncover the root cause of these challenges and analyze if it can be solved internally or if external help or additional tools are required. Recent reports suggest that most business challenges arise because organizations are unable to create value for its customers, employees, and the overall market. The reasons can be many, one being a lack of trend analysis making it difficult for businesses to create a competitive advantage.
  2. Identify and select the challenges Big Data can solve Data, when used strategically, becomes actionable insights that can solve critical challenges unearthed in step 1 above, drive powerful efficiencies or uncover potential growth opportunities. These insights can have a profound impact on strategies across the organization from sales, marketing, production, finance to customer service. When reviewing challenges around productivity, customer experience, employee satisfaction, and more, it is important to keep an open mind while analyzing the data. This helps to uncover what the data really has to SHOW verses what we want to SEE. By triangulating data sources, companies can predict what they need to be more efficient and can then make decisions accordingly. However, decision makers should not depend entirely on modern-day modelling techniques. The best combination is employing new predictive models and combining them with traditional techniques and A/B testing to scrutinize results. A Big Data consulting and analytics partner can also help guide you during this analytical process to answer questions like – what and how to tweak the models, where and how to allocate right amount of resources, what and how to monitor, and how to equip people to be most effective?
  3. Discovery and management of data sources A vital part of big data analytics is a deep understanding of where all your pertinent data lies. Data may be in various forms such as – IoT data (Computer, biometric devices and mobile device log files), sensor data, SaaS and cloud applications, data warehouse apps, Hadoop / MapR applications, social media profiles, legacy applications, social influencers and more. Many organizations have data silos managed by different business units or leaders. There is hidden value in these data repositories if they can be brought together effectively. Organizations should encourage a strong data management and analysis culture to ensure the effective and centralized use of this data via big data tools to make a meaningful impact.
  4. Don’t settle with the conventional approachAccurately measuring operational effectiveness will have vital implications for an organization. Employees should be educated on data streams and consolidations that can create an impact. Companies that only use traditional attribution approaches may fail to detect the discrepancies that could be avoided. Big data analytics tools and business intelligence offers features from ingestion through representation and help predict what might result (i.e. what reaction a specific action would generate, or how much would X affect Y action). Following conventional isn’t wrong, but it may miss opportunities to drive vast improvements or business profits. A review of the analytical models that big data tools can offer may open up avenues previously undiscovered.
  5. Optimal utilization of big data tools A key step in deriving value from your data is selecting a platform that can absorb, manage and analyze the data. The right tools will be based on your data, specific goals and potentially your internal capabilities unless you have a partner that can help you manage the initiative. You will need various tools for implementation, e.g.- Data Storage and management, data cleaning, Data Mining, Analysis, Visualization, Integration, Languages, and Data Collection etc. The big data tools you are currently using or evaluating should also evolve over time, just like your business. Your data analytics professionals should also have a comprehensive knowledge of your tool sets and have the mindset to look beyond rational boundaries. To get the most out of the tools, analyze the data in different combinations to identify ways to solve your challenges. Predefine the appropriate modeling, reporting metrics and key performance indicators (KPIs) across the entire organization so that stakeholders are well-equipped to analyze the data and make reliable decisions. Consistency in your data management process is very important.

Big data Analytics as a discipline is ever evolving and unfortunately there are no one-size fits all solutions. However, following the steps discussed in this blog will put you on the right path toward deriving value from your big data initiatives.

If you are looking uncover actionable insights from your big data but unsure where to begin, connect with our team of Big data solution experts to schedule a conversation or assessment.

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