Faster Delivery = Happy Users
Automated Process = Fewer Errors
Standards = Cost Reduction
Order Visibility = Confidence
Linking Systems = Efficiency
According to International Data Corp (IDC), nearly 1 in 3 Canadian organizations will begin either experimenting with Big Data or will enter the proof-of-concept stage this year. A further 1 in 5 will progress beyond that point into production. This is indicative of the fact that Big Data has reached an inflection point in the country and is transitioning rapidly into the mainstream.
While worries about Big Data security remain, these appear to be diminishing as more companies gain familiarity with the technology. In 2015, the top concern among Canadian organizations about Big Data was security. In 2016, however, the inability to adapt to the unplanned and indiscriminate nature of Big Data posed the biggest challenge to IT managers. Thus, as adoption rates grow, agility has overtaken security as the main challenge. The good news, however, is that the volume of information and velocity of creation and transmission are no longer major issues impacting Canadian organizations. They have been coming to terms with escalating capacity and throughput totals and are putting the technology in place to capitalize on Big Data.
In light of these shifting market dynamics, Softchoice and Dell interviewed IDC to find out what organizations should do to prepare. The full interview is covered in Big Data Inflection in Canada: From Experiment to Platform by David Senf, Program Vice President, at IDC’s Infrastructure Solutions Group.
Here is a summary of the key points to prepare for the Big Data boom:
Where should organizations start in terms of considering a Big Data project? The best starting point is to define its purpose. Typical goals for IT projects include reducing costs, growing revenue, improving service delivery and reducing risk. They should be targeted as closely as possible at key business or logistics areas of strategic importance in order to align the initial foray into Big Data with ongoing priorities. Finance is typically at the top of the list for Canadian organizations, with improved product quality, product development, and service optimization close behind.
IDC reports that the average length of big data projects in Canada is 14 months. Phase 1 is data creation, which includes collection of sensors, clickstream and ERP data. That is followed by a phase of data acquisition; analysts and data engineers gather data from a range of sources, clean it and address quality. The number of data sources varies, but Canadian organizations typically use transactional data from ERP, CRM, and other enterprise systems. Website logs and social media are also becoming popular. The third phase is information processing where analysts and data scientists create models, explore data and make forecasts. The final phase is business processing. This is the point where the hard work pays off and the information can be used by the business to make better decisions.
A well-executed Big Data plan can lead to faster deployment and increased revenue. But it must be based upon the right strategy to align well with business goals. Further, the business, the analytics and the IT teams must all participate to ensure project success.
Finance, sales, marketing, production and even HR will have input on how to collect and harness information for business success. Clearly, interest will be high for any Big Data initiative, both at a top management as well as at the departmental level. Coupled with a lack of familiarity on how to translate high quantities of data into actionable insight, it can often be difficult to determine how to leverage current infrastructure and achieve a high return on investment.
That’s where total visibility into the existing IT infrastructure and storage environment comes in. Once an organization understands this, it is better equipped to formulate an IT plan to maximize returns from Big Data projects.
Softchoice strongly recommends starting with full visibility into every corner of IT. This is accomplished through a Datacenter TechCheck Assessment – an ideal starting point for those embarking on a transformational Big Data journey.