Effective Big Data Strategy
CIOREVIEW >> Big Data >> NEWS

Effective Big Data Strategy

CIO Review

There has been a significant increase in the use of big data analytics and the adoption is expected to grow even more. While web companies have already devised and deployed big data strategy, brick and mortar companies are still grappling with it. George Lumpkin, VP, Product Management at Oracle opines that enterprises must be quick in learning how to leverage big data as the amount of data being generated shows no signs of slowing down. In the high-octane technology landscape, implementing big data analytics can be cumbersome. Right strategy coupled with a thorough understanding of business imperative is a must.

Stay Focused

In order to avoid getting swayed by the buzz around big data, organizations should ensure that the end goals are cemented in their big data strategy. Organizations should utilize big data to satiate the real world business needs. Strategic planning for big data projects must be business-driven with IT leadership engaged and informed about the entirety of the process. Specific business problem, use cases, and market opportunities should be the cornerstone of any big data strategy.

Small Start

The biggest mistake that businesses make when it comes to formulating an effective big data strategy is going too far and fast. While organizations might succumb under the constant pressure of analyzing all the data at company's disposal, going fast makes big data projects lose its flare.

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Adopting the 'crawl-walk-run' principle will prove fructuous to steer big data innovations in an organization. Short-term, well-defined, and measurable goals in conjunction with constant check on the growth velocity are the key. As the big data maturity of the organization increases, further expansion and long-term objective can be actualized.

Ensuring Better Data Quality

The massive datasets analyzed through the big data tools often raises eyebrows on data quality. Organizations should realize that not all data is perfect, and it is not unusual. Big data does not always warrant perfect data quality. The real imperative is to match the data quality with the application using them. 

Flexibility

The massive proliferation of big data has impacted everyone's workaday life. However, for organizations to reap most benefit out of this disruptive force, they need to harness it for the future. Strategies and methodologies must avoid constraints such as excessive reliance on single technology, among others. The big data driven transformation must be gradual and incremental ensuring value creation and augmenting product quality.

Traversing Beyond In-House Data Repository

Most businesses tend to confine the scope of big data within organization's boundary. However, to augment business values and reap most benefits, businesses have to dissolve the boundaries of in-house data repository. The interconnected world of data is much larger and certainly can be leveraged to derive maximum profits.

Data is increasingly becoming liquid. And while thinking about big data and its use, it is also imperative to ponder upon the data that an organization has access to, rather than the huge dataset that they own.

Adopt Outcome Oriented Approach

It is crucial for organizations to adopt an outcome oriented approach to formulate an effective big data strategy. Thinking beyond pilot is a must to ensure big data strategies are enforced in the right way without creating another data silo. A welcome step for most organizations is to fabricate a powerful and adaptable ecosystem that conjoins data discovery and data platforms ensuring long-term scalability and connecting to mission-critical external data sources at the same time.

Upcoming technologies that cater to big data would also empower organizations with the capability to do analysis that hitherto seemed unfeasible. By adding them to the mix, organizations can leverage best of structured and unstructured datasets. These developments certainty would ease off business impediments, and draw organizations closer to their customers.

More in News

Distributors can have plenty of inventory across their network and still struggle to meet demand at individual branches. Stock sitting in one warehouse does little good when customers need it elsewhere, while keeping every location stocked for its highest historical demand can tie up unnecessary capital. With thousands of product and location combinations to manage, purchasing teams cannot review every decision manually. Executives evaluating demand forecasting software need to know whether it can improve local product availability without driving up inventory costs across the business. Network-wide averages can easily hide costly gaps at individual locations. Past sales provide a useful starting point, but irregular buying patterns can make forecasting difficult. Slow-moving products may not generate enough transactions to produce a reliable forecast on their own. Seasonal spikes can also skew averages long after demand has returned to normal. Buyers should look at whether the software can draw insights from related products and distinguish recurring demand from unusual orders. External factors such as weather may also help improve predictions, but their value needs to be demonstrated for the products concerned rather than assumed across the entire catalog. Knowing what customers will buy is only half the equation. Buyers also need to know when to reorder. Changing supplier lead times can make it difficult to act on even an accurate demand forecast, while order frequency and minimum purchase quantities affect how much stock needs to be kept on hand. Software should account for actual purchase order history rather than rely solely on fixed lead times. Safety stock should also reflect how individual products sell at each location. Applying the same buffer everywhere can leave fast-selling products out of stock while slower-moving items pile up. “Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them.” The way forecasting software works with existing ERP systems is another important consideration. If recommendations sit in a separate dashboard, buyers may still have to enter them manually into their purchasing systems. They should establish whether the software can read transactions from their existing ERP and feed approved stocking parameters or purchase orders back into it. Buyers also need to understand why the system recommends a particular change. When a business wins a new account or a branch receives an unusually large order, experienced staff should be able to override a forecast without having to work outside the system. Forecast accuracy matters, but it does not tell the whole story. Lower inventory means little if it leads to stockouts and unhappy customers. In some cases, holding more stock may be the right decision if those products sell consistently. Buyers should look at how the software affects product availability, inventory turns and the capital tied up in excess stock. Testing its recommendations against past transactions can also show where they differ from existing purchasing practices before a wider rollout. Buyers should also account for the work involved in preparing historical data and managing exceptions once the system is in place. Lantern builds a custom machine learning forecasting model for each distributor, using historical ERP data and business-specific purchasing practices. It combines demand forecasts with projected supplier lead times to recommend order timing and safety stock at the item-location level. Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them. Approved purchase orders or stocking parameters can be written back into the ERP. Its models also examine relationships across products, supporting forecasts where individual sales histories are sparse. Distributors with large catalogs spread across multiple branches, especially those balancing excess inventory against missed sales, are the relevant buyers for this model. ...Read more
Enterprise imaging purchases rarely fail because a scanner cannot create a digital file. Trouble starts when document volumes and legacy applications collide with downstream workflows that were built around older processes. A platform may handle clean, standardized forms well, yet slow down when incoming files vary substantially or demand different routing rules. Executives, therefore, need to examine how completely a proposed environment can absorb the real document mix without forcing extensive manual preparation or creating new bottlenecks elsewhere. Capture architecture deserves close attention as the front end determines how much work remains after scanning. Useful systems should support classification, optical character recognition, data extraction and validation in ways that match actual document types and exception rates. Human review also matters. Automated recognition is valuable when confidence is high, but buyers should understand where validation screens enter the process and how questionable fields are routed before information reaches a database or content repository. Hardware choices belong in the same discussion. Desktop scanning and high-volume production work impose very different demands, and forcing both through a single equipment model can inflate costs or limit throughput. The proposed design should reflect actual preparation effort, peak intake volumes and the physical scanning environment rather than nominal device speed alone. Scanner flexibility also matters where several departments process different document sets at substantially different volumes. “EDAC Systems provides first-line support across the hardware and software it sells, reducing the need for customers to separate faults among multiple providers.” Legacy compatibility is equally consequential. Many enterprises still depend on older capture software and long-standing repositories while retaining scanners embedded in established processes. Replacing everything at once can increase implementation risk and budget pressure. A stronger proposal should account for migration and configuration from the outset, with integration planned around how existing records move into the new environment and how the changeover will be tested. Buyers should also examine whether the architecture can adjust when document volumes increase or automation requirements change without requiring a wholesale rebuild. That flexibility becomes more important when an organization expects its capture requirements to shift over several budget cycles. Security and records requirements can narrow the field quickly, particularly in government or regulated environments. Encryption and records-management controls need to be resolved against approved deployment conditions before installation, rather than treated as post-deployment configuration. Procurement teams should verify that both hardware and software satisfy applicable requirements and that the provider can work within on-premises or approved hosted environments when needed. Support after go-live is another practical separator. Document imaging environments combine scanners, capture software, connectors and repositories, which can leave customers coordinating multiple suppliers when something fails. Contract review should establish who owns first-line troubleshooting and whether hardware and software issues are handled through one support path. Availability of on-site assistance also matters when remote fixes are insufficient. EDAC Systems designs document imaging and data capture environments around each customer’s document types, workflow, existing technology and budget constraints. Its approach can combine Ricoh or OPEX scanning hardware with capture software and Doxis-based document or records management, depending on the application. It also handles migration, configuration, testing and training during implementation. It provides first-line support across the hardware and software it sells, reducing the need for customers to separate faults among multiple providers. For enterprises with mixed legacy environments, compliance requirements or varied document volumes, the breadth of design and support makes EDAC Systems a practical vendor to consider. ...Read more
Moving from Google Workspace or legacy Exchange to Microsoft 365 involves more than transferring mailboxes. File structures may not translate cleanly, account permissions can change and an overlooked dependency can interrupt access after cutover. Executives comparing migration providers must establish how thoroughly each one investigates the existing environment before committing to a transfer schedule. A technically completed migration offers little reassurance when employees cannot retrieve the information they previously used. Pre-migration assessment establishes the boundaries of the work. User inventories should account for inactive accounts and shared mailboxes, while file assessments must identify naming restrictions, excessive path lengths and permission structures that Microsoft 365 handles differently. Tenant-to-tenant transfers introduce additional complications around identity mapping and existing collaboration spaces. Providers should document these differences before migration begins rather than discovering them during execution. Compliance requirements can alter the migration design considerably. Retention policies and access restrictions may determine where information resides after transfer or which users can retrieve it. An assessment that concentrates exclusively on data volume risks overlooking controls that governed the original environment. Procurement teams benefit from examining whether the provider produces a documented migration scope connecting technical dependencies to the organization's internal requirements. “RyanTech incorporates Microsoft Defender and Purview into tenant planning, allowing security requirements to shape the destination environment before users begin working in it.” Cutover scheduling exposes a different set of risks. DNS propagation may take longer than anticipated, while network interruptions can delay synchronization during a planned transition. Promises of zero downtime leave little room for these external dependencies. A credible migration approach includes contingency procedures, defined escalation responsibilities and a cutover window that reflects the organization's working patterns. The provider should explain what happens when an expected transition does not occur on schedule. Business continuity also depends on what employees encounter after the switch. Missing shared files or unfamiliar permissions can generate support requests even when mailbox transfers succeed. Validation should establish that users can access the resources required for their work, not merely confirm that data reached its destination. Clear responsibility for resolving post-cutover issues reduces the burden on internal IT teams. Security configuration cannot be deferred until employees have settled into Microsoft 365. A newly provisioned tenant may introduce unfamiliar exposure through identity settings or insufficiently configured data protection policies. Microsoft Defender, Intune and Purview provide controls that can be incorporated into the destination design. Providers differ in whether they configure these protections during migration planning or treat them as later projects. Licensing decisions also influence which controls are available and whether organizations purchase functionality they will actually use. Support arrangements become particularly relevant once the migration specialists have completed their assignment. Changes to compliance requirements or user access may expose decisions made during the original transfer. Continuity between migration and support personnel helps retain that context. RyanTech conducts Microsoft 365 migrations through an initial environment audit that establishes customer requirements and informs a documented transfer plan. Its migration team prepares for DNS delays and other cutover interruptions, scheduling transitions around business requirements rather than promising uninterrupted service. RyanTech incorporates Microsoft Defender and Purview into tenant planning, allowing security requirements to shape the destination environment before users begin working in it. Its project and support teams subsequently address configuration changes, user adoption and evolving compliance requirements. Experience spanning Google Workspace, on-premises Exchange and tenant-to-tenant migrations informs its handling of differing source environments. Organizations consolidating disparate systems into Microsoft 365 while requiring security configuration and continuing technical support are particularly suited to this approach. ...Read more
The management of cloud environments involves the proper coordination of infrastructure, access rights and resource allocation as companies expand their operations digitally. In Latin America, organizations adopting cloud-based systems must account for regional data protection requirements, varying infrastructure capabilities and the complexity of overseeing multiple service providers. Cloud management is an effective tool that enables the IT professionals to control the workloads, ensure the standard configuration, resource consumption and detect the possible disruption of services that could impact critical business operations. Clear governance policies, automated monitoring and well-defined access controls can help businesses maintain reliable cloud environments while managing operational complexity. How Does Cloud Management Benefit Businesses? Cloud management makes it possible for companies to have more options when dealing with varying loads without making significant investments in infrastructure. Organizations can scale computing capacity to match project demands, support remote teams and deploy digital services across different locations. For companies operating in Latin America, this flexibility can help accommodate expansion into new markets while allowing technology resources to evolve alongside business requirements. The other significant benefit relates to visibility in finances. Centralized oversight helps organizations understand spending across cloud services, identify underused resources and select suitable service plans. These insights support more informed budgeting and reduce the likelihood of paying for capacity that delivers little practical value. Businesses can also evaluate service usage against departmental needs to allocate technology budgets more effectively. Cloud management can be an important tool to improve the continuity and collaboration of business operations. Backup planning, recovery procedures and coordinated service administration help organizations prepare for unexpected outages and restore essential applications when disruptions occur. Shared digital workspaces and accessible business applications can further support collaboration among distributed teams. For businesses in Latin America, these capabilities can help maintain consistent operations across regional offices while supporting day-to-day productivity. What Are the Emerging Trends in Cloud Management? Artificial intelligence is reshaping cloud management through intelligent analytics, predictive monitoring and automated decision-making. These tools can help IT teams identify unusual performance patterns, anticipate capacity demands and investigate potential technical issues before they escalate. In Latin America, growing interest in AI-enabled infrastructure management is creating opportunities for businesses to simplify complex technology environments while improving the speed and precision of routine IT operations. The other emerging trend is the increasing adoption of hybrid and multi-cloud solutions, which combine private infrastructure with public clouds and services offered by several different providers. FinOps approaches are another area where enterprises are increasingly focusing, which involves financial decision-making around cloud usage, along with security methods that keep a check on the risks to which access is exposed. For businesses in Latin America, these developments highlight the need for cloud strategies that balance technical flexibility, cost transparency, regulatory requirements and evolving cybersecurity risks. Together, these approaches are shaping a more intelligent and adaptable future for cloud management.  ...Read more