Many organizations have a wealth of data at their fingertips but struggle to harness it. The problem? It’s often spread across many disparate systems and those accessing it are not empowered to gain actionable insights from it.
The organizations who have been able to successfully harness big data, however, are reaping major benefits. These organizations are the ones who began building a multi-structured, mass storage of data before they even knew its worth. Today, they are using this data to improve their operational efficiency, grow their revenues and innovate new business models.
What did they do to enable such effective big data leveraging early on? According to Tableau, they applied the following seven best practices:
1. Think long term by thinking short term.
2. See through the false choice.
3. Bring big data down to eye level.
4. Empower users for big insights.
5. Make bigger data out of small data.
6. Ensure that big data stays out of big trouble.
7. Get the ball rolling.
Let’s explore each a bit more deeply.
1. Think long term by thinking short term
While some organizations get caught up constantly trying to keep pace with big data technology development, others recognize that big data technology will only continue to rapidly evolve for specific usage purposes and system integrations.
Rather than getting overwhelmed by this rapid evolution, stay open to the possibilities. Maintain a business intelligence base that can readily connect to a variety of formats so that when a product comes to market that meets your needs exactly, you are ready to integrate.
2. See through the false choice
While your organization may wonder, which do we need—Hadoop or a data warehouse, more often the more appropriate question to ask is how we can create a symbiotic relationship between the two. Organizations often feel that is a one or the other decision when trying to determine which data management systems to employ, when it most cases it should be a thought process of how to make several purpose-driven systems effectively co-exist.
For example, according to Tableau, a data warehouse is best for storing important, structured data that business intelligence tools and dashboards can easily connect to, but because data warehouses are often slower and weaker for analytics processing, Hadoop can be brought in to fill the gap. Hadoop is great at ingesting unstructured, complex data and transforming into refined, restructured data models that actionable insights can be obtained from.
3. Bring data down to eye level
Being able to visualize your data is essential. Visual data discovery enables staff members to find the information they need without the help of IT staff and to interact with data more intimately in being able to view it a manner that easily allow stories, trends and patterns to unfold.
Visual data analysis allows you to change the data you’re looking at to answer different questions as well as change the view of it to derive different answers—it enables your business staff to become data scientists as they realize how as their inquiries deepen, so too do their insights.
4. Empower users for big insights
There’s simply no time now for anything but self-service data analysis. Rather than waiting for data to be cleansed, processed and published by IT as if chapters in a book that only they can decipher, allow your business intelligence to be driven by your business community.
Invest in big data technologies that can allow business users to flexibly examine and discover the value of your data for you and once that value has been identified, then it be can sent through the more rigorous data warehouse and publication processes.
5. Make big data out of small data
Big data is made out of many smaller data sets and when stitched together, big value can be found. One cannot gain a holistic understanding of the consumer journey until data is layered on top of data.
Organizations that are able to blend relational, semi-structured and raw data to gain consumer insights are able to gain the most value. No matter where your data is housed, you need the flexibility to connect and consolidate it in order to gain the full picture view.
6. Ensure that big data stays out of trouble
From Sarbanes-Oxley to HIPAA, with big data comes even bigger compliance concerns and the need for master data management.
Master data management requires governance—it requires an information management system where set definitions and business rules are in place for managing data of varying degrees of security. But, rather than adhering to a single set of standards, policies and practices that can stifle value discovery, instead consider adopting governance that matches specific analytic capabilities and objectives. For example, establish different governance zones, considering the data source and types that falls under that zone, and implement specific rules for each.
7. Get the ball rolling
The last tip could be the most important: Just do it. Just jump in and follow the other six steps. Big data’s already at your doorstep, if not inside. Go for results now.
“I can answer things within a business meeting at the speed we’re going at now,” says Peter Gilks of Barclays. “Before, we were talking a day or two turnaround per question. Now I can sit with my laptop in a meeting and answer questions on 20 million rows of data basically on the fly.”
Whether your goal is better leverage data to gain a more comprehensive customer view, personalize your customer interactions or support data-driven decisioning across the supply chain, Cierant can be counted on to design a custom BI dashboard that meets your goals.
Call 203-731-3555 or email firstname.lastname@example.org to demo our business intelligence dashboards at work and explore how we can design one that meets your unique analytics objectives.