In IT circles, the migration of legacy workloads to elastic cloud infrastructure is a hot topic – but the related data migration and modernization effort could be equally transformative. Then why does deciding what data to migrate “as is” get prioritized over how to best use and optimize that data once it’s in the cloud? Many organizations simply replicate their siloed, legacy data into cloud silos, but doing so is a missed opportunity.
When properly interconnected and fed into data lakes in the cloud, your siloed legacy data can provide valuable inputs for business decisions, especially when combined with artificial intelligence (AI)–driven insights. It can also deliver better performance and cost savings and be a source for continuous innovation. Therefore, consider building a data modernization strategy into your digital transformation plans to ensure your cloud investments yield the highest return.
The primary way to create synergistic analysis among data sets is to merge them into a cloud-based data lake. Data lakes can include structured (e.g., relational databases, ERP systems, Excel) and unstructured data (e.g., social, email). Deep analysis within the lake can cross-reference data points against seemingly disparate pools to find unexpected and highly valuable opportunities. Leaders of data modernization projects must therefore consider and answer four pivotal questions:
A successful data modernization strategy must start with helping to create internal synergies among business units (BUs) in real time. The more data is available for analysis across business units, the better the chances for positive outcomes. To do this, break down the silos and create a data lake. By doing so, many synergies and optimizations will become readily apparent.
For example, insight about current customer demand is critical to inform the manufacturing process, but so are predictive insights about customer behavior buried in marketing data and forecasts. In turn, customer insights, production schedules and product roadmaps help HR adapt hiring and training accordingly.
This is one scenario among many that illustrate the point. The bottom line is this – in a large organization with different functional units, linking the data and insights of each across the enterprise delivers mutual benefit for the units and the enterprise at large. Depending on the industry, the organization could derive cognitive insights based on enterprise data lakes for myriad use cases, including personalizing customer experiences, detecting fraud, actuarial modeling and predicting failures in mission-critical equipment.
Therefore, a data migration plan for when to populate your data lake in the cloud and what you should populate it with should be driven by business strategies that foster cross-business-unit innovation.
IDC reports that by 2025, it expects 175 zettabytes of data to be created worldwide each year, which is approximately four times the amount produced in 2020. That is what is known as an embarrassment of riches. How much of it is high impact and high value? How long should it be retained? Organizations face the scourge of “three Vs” of their stored data – volume, variety and velocity. The sheer amount of data coming in, the varied forms it takes (video, audio, text, social media, etc.) and its velocity – especially that arriving from IoT devices, threaten to diminish its utility unless wisely curated.
Legacy data warehouses are often bound by available disk space that must be continually and individually expanded while ensuring that performance is not degraded as scaling up occurs. While the cloud theoretically provides unlimited storage space, it comes at a cost, and a good data management policy is necessary to ensure archival to less expensive storage tiers and purging of outdated data. For example, older transactional and log data may be “aged out” to preserve space and performance.
With well-engineered cloud-based data lakes, you have an elastic, agile volume that accommodates all the challenges of the “three Vs” at a fraction of the cost of siloed warehousing.
Trained data specialists, along with expert strategic planning of your cloud data repository, are vital to getting immediate and lasting benefits from your data lake initiative. It is also essential to approach modernization with a keen eye on its continual and ongoing evolution.
Data scientists using deep AI analysis can turn a potentially fruitless data-fishing expedition into a targeted, valuable strike. Gartner has found that when chief data officers (CDOs) are involved in setting goals and strategies, they can increase consistent production of business value by a factor of 2.6X. What’s more, the demand for data-related expertise is huge and growing: LinkedIn’s co-founder reports that data science and machine learning–related jobs, taken together, represent five of the top 15 growing jobs in America today.
Without skilled professionals minding your data strategy, your data lake will devolve into a data swamp – polluted by outdated data that provides faulty or low-value insights if utilized. Consider, for example, HR’s obligations to plan the workforce needed to carry out the company’s strategy for a global organization. They need access to data that tells them such things as how many students are pursuing degrees in the fields the company will need and in which countries they are concentrated. They need to know whether the jobs will be remote or on-site. Clearly, if they were using outdated (e.g., pre-pandemic) data, they would not know to capture data about remote vs. on-site preferences. Data operations (DataOps) and machine learning operations (MLOps) are relatively early in their adoption cycle cycle and are critical for operationalizing and optimizing these workflows.
Therefore, make certain your cloud data lake and analysis tools can scale to demand in real time. You must onboard or outsource expertise to data experts to get real value from your modernization efforts.
Cloud data lakes, with the right security controls, are critical for ensuring compliance and data sovereignty. However, it is essential to build security into the initial framework of the data lake implementation for the highest level of security and integrity. Best practices include utilizing the tools from cloud providers and security vendors with hardened processes for ensuring security, including encryption, zero trust access management and continuous real-time monitoring and remediation. Affordable, geolocated cloud mirroring and data backup are also widely available.
The obligation to protect sensitive and personal information is a heavy cybersecurity responsibility. Missteps and breaches are met with costly fines, reputational damage and customer mistrust. Organizations need to rigorously focus on stripping out personally identifiable information (PII) and health data and comply with regulations that vary widely across industries and countries. Fortunately, storing data in the cloud provides a strong measure of security. Software-defined micro-segmentation offers multiple security controls to manage who has access, their roles, where to store data and so on.
Your cloud-based data lakes with built-in security are more secure and resilient and have greater performance and agility than siloed legacy data solutions.
How Unisys Can Help
Unisys takes a holistic approach to data modernization. We help organizations formulate a data-first strategy for business success, recommend which data silos to migrate to a secure data lake, how to populate and keep it updated continually, and surmount the onerous restrictions of volume, velocity, and variety of data. In addition, our data analysis experts, using targeted AI, help organizations discover improvements in operations and costs, raise customer experience satisfaction and empower true cross-BU innovation. To learn more, visit us online or contact us today.