3 Tips to Ensure Your Data Analytics Platform Is Future-Ready

As enterprises rushed to digitize their offline assets over the past year, data finally won widespread recognition as the lifeblood of modern commerce. At a high level, data enabled pandemic-shocked businesses to comprehensively understand their changing landscapes, then generate actionable predictions for how demand would shift. There’s little question that data has grown in importance every year, but in 2021, there’s now no question that its value is crystal clear to virtually every enterprise.

Looking ahead to a new, post-pandemic era, data will be critical to enabling a full economic recovery. Many organizations will use data analytics to better meet the needs of their customers, improving their operational agility and responsiveness without increasing their costs. Wise enterprises will go further, using data to prepare their people and profit centers for constant change.

While no one can fully predict the future, history suggests that businesses should anticipate demand for new levels of speed, agility, flexibility, and innovation, all powered in the digital era by advanced data processing. Consequently, companies should place data analytics at the center of their digital plans, as data and analytics will be vital for navigating new opportunities and challenges of any magnitude.

Forward-looking leaders should choose a data analytics solution that is future-ready, capable of addressing both today’s and tomorrow’s needs. To that end, here are three top tips for ensuring your business selects a dynamic data analytics platform that will meet whatever demands the future may bring.

TIP #1: Today’s “Best-in-Class” design is not good enough for tomorrow

Every competitive company would prefer to have “the best” data analytics platform – one that uses data to its fullest potential, helping to clarify or perhaps automate business decisions that will rapidly enable higher revenues and profits.

However, since demands for scale, complexity, and speed are likely to increase each year, perhaps exponentially, investing in today’s supposedly “best-in-class” data analytic solutions will ultimately fall short of what your future enterprise truly needs.

The pursuit of best-in-class solutions can result in tunnel vision, focusing on one key metric at the expense of others. For example, the “best way” to store data at the cheapest cost, look to improve the capabilities of a single siloed department, or otherwise fixate on one specific feature that’s important today, while overlooking what may matter more in the future.

Future-ready enterprises re-envision the ongoing data journey; they equip themselves now for the rapidly expanding demands ahead. Instead of focusing on how data is used today, they prepare for a tomorrow where personalization will impact everything from SKUs to stores and customers, requiring access to granular data and the ability to manage increasing flows and variations from the top down.

What will the enterprise of the future look like? As just one example, retailers will leverage big data and analytics, gathering local “edge” data to react rapidly to store-level changes, as well as constantly inform strategic, future-focused decisions. Analytics will process hundreds of millions of queries per day just to generate demand and supply forecasts. And rather than siloing data, the analytics platform will be open to partners and suppliers – in some cases, customers as well – so the total number of users could be measured in the millions, even if some or many of those users are infrequent.

Tomorrow’s business leaders won’t be stuck with static strategies or narrowly-focused analytic platforms. They will leverage data to plan and course-correct their journeys, frequently considering their best micro- and macro-level responses to the shifting demands of “always-on” digital markets.

TIP #2: Hyper-scale data orchestration is key

Future-ready enterprises must orchestrate data analytics to solve problems and drive informed, actionable decision-making. Orchestration allows the enterprise to reuse data as part of a modern, robust data analytics ecosystem, coordinating every initiative to drive growth and value.

Traditional approaches may use partial, siloed, or incomplete data to make decisions; future-ready enterprises will integrate all data before making decisions, then act on those decisions.

Data analytic orchestration incorporates many systems, functions, and data types, operating at hyper-scale. To achieve this, the platform must enable multidimensional scalability, addressing eight core dimensions:

  • Data volume
  • Query concurrency
  • Query complexity
  • Schema sophistication
  • Query data volume
  • Query response time
  • Data latency
  • Mixed workload

Multidimensional scalability delivers the advanced flexibility future-ready enterprises need to run millions of productized models on trillions of interactions, every second of every day. Equipped with these next-level data insights, companies can make decisions with perspectives and scale that were previously impossible to achieve.

TIP #3: Today’s data must be put to work as insights, preparing for tomorrow’s “outsights”

Times are changing, and each organization’s data analytics leaders must learn – or re-learn – how to expand their use of data and analytics for greatest impact. For future-ready enterprises, this will mean infusing analytics into every role, business process, decision, and action. Enabling data to work not only between people but also between systems will help the entire enterprise achieve operational intelligence.

As of today, global companies understand the value in shifting from traditional analytics to predictive and other advanced analytics, such as artificial intelligence (AI) and machine learning. But future-ready enterprises take the next step, leveraging these technologies to predict problems, make effective decisions, and rapidly adapt their operations. Going forward, AI will go beyond leveraging analytics to alert the company about what is going to happen next; it will use prescriptive analytics to take autonomous corrective action. 

Companies will not be limited to relying upon their own data, either. Enterprises of the future will ingest, integrate, and incorporate external data along with internal data, allowing analytics users to consider both “insights” and “outsights.” Insights, which are achieved when companies analyze their own data, will help drive the business. Outsights will be even more valuable, as they will be derived from more data sources, enabling insights to be contextualized or contrasted as needed.

The future belongs to enterprises that allow data to lead the way

As we all work to move past the challenges of the pandemic, businesses must understand that we are now officially in the digital era: Data is now as critical as revenues, profitability, and customer experience in ensuring any enterprise’s future viability, regardless of what tomorrow may have in store.

Properly embracing data’s massive potential requires big picture, longer-term thinking Future-ready enterprises will be prepared not just for analyzing data today, but also for the inevitable shift to integrating all data, inside and outside the organization. Gleaning insights from a modern data analytics platform will enable companies to hyper-scale their ambitions, harnessing all of their data to take actions that would not have been possible with narrow insights.

Companies that embrace a data-first mentality will not just rebuild – they will completely reimagine their businesses. In doing so, they will invest in and benefit from unified data ecosystems, enabling advanced analytics to better understand their markets, their operations, and their customers.

Data is the lifeblood of modern commerce. The sooner an enterprise realizes how much data and scalable analytics will make a difference in results, the faster it can adopt an advanced platform capable of forecasting the future, and chart a new course to greater success. 


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