Consider a simple example of going for a movie with your family: you send a tweet that you are going for a movie and create data; you browse through the list of movies and theaters and access data; you purchase tickets online and create data; you fill fuel on your way and swipe your card to pay, and create data; you post a review on the movie on Facebook and create data.
Just as you create and work with data throughout this activity, your credit card company, the movie theatre and fuel station also juggle with terra bytes of business and customer data on a daily basis. While this data can pose several challenges for businesses, it creates opportunities for organizations to gain insight into their customer’s buying preferences and choices.
According to a recent research report published by Gartner, Big data forces businesses to wrestle with three key strategic and operational challenges: Information Strategy, Data Analytics and Enterprise Information Management.
Here is a more detailed explanation from Gartner on each of these operational challenges. These illustrations can benefit our customers when they formulate big data strategies for their organizations. I would highly recommend all of us to read these explanations and use these references while answering big data related questions we face from our customers.
Enterprises need to harness the power of information assets. Big data is causing enterprises to find new ways to leverage information sources to drive growth. When trying to identify ways in which their enterprise can benefit from big data and why it’s important, they need to make the strategic decisions that will transform their business.
Strategy: Is their organization prepared for an information-led transformation? How will they harness big data to improve strategic decision making? They need to know which investments will deliver the most business value and ROI.
Governance: How will they govern their organization’s information assets in support of their enterprise information management (EIM) goal? Are there new expectations for information quality and management?
Talent: By 2015, Gartner predicts that 4.4 million jobs will be created around big data. Does their organization lack the “data scientist” talent required to exploit big data? How will they assemble the right teams and align skills?
Businesses need to draw more insight from big data analytics or large and complex datasets. They need to predict future customer behaviors, trends and outcomes. IT is under pressure to tap into growing quantities of data to help the business make better, informed decisions by combining new sources of big data with existing enterprise dark data. How will they uncover more customer and business insight and more data value?
Predictive Analytics: How are they using data for predictive and real-time analysis across various business domains? How can they use unstructured enterprise information and data to drive a better client experience? How are they leveraging new data types: sentiment data, clickstream data, video, images and text data?
Behavioral Analytics: How will they tap into complex data sets to create new models to drive business outcomes, decrease costs, drive innovation or improve customer satisfaction?
Data Interpretation: What new business analysis can be drawn from their data? How will IT help support insight discovery and new information trends? They need to know which data to integrate for new product innovation.
Enterprise Information Management
Information is everywhere – volume, variety, velocity – and it keeps growing. Organizations need to manage access to growing extreme information management requirements and drive innovation in rapid information processing. What are they doing with all of the data the organization collects? They have many disparate sources – from their enterprise’s “dark data” and partner, employee, customer and supplier data to public, commercial and social media data – that they need to link and exploit to its fullest value:
User expectations: Employees are demanding more access to big data sources. What’s their plan to manage access to these information sources? What are the use cases?
Costs: How can they deliver access to big data in a rapid and cost-effective way to support better decision-making?
Tools: How will they link these new sources of diverse data? They need to plan the impact on the data center. Have they identified the processes, tools and technologies they need to support big data in their enterprise?
Mckinsey calls big data “the next frontier of innovation”. Evidently, the challenges with big data are limited when compared to the opportunities it creates and the potential benefits it offers. Our creativity, knowledge and ability to contextualize and understand this data is key to deriving value from it.