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Actian Blog / Actian Datacast: Are you leveraging the right data for the right decision-making?

Actian Datacast: Are you leveraging the right data for the right decision-making?

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Last week we announced the findings of the Actian Datacast 2019: Hybrid Data Trends Snapshot, sharing insights into the current challenges as well as opportunities for data-driven enterprises around managing hybrid data environments.

Our survey polled 303 IT professionals with influence and or decision-making ability at their companies of 500+ employees.

As the first installment of our blog series exploring the four key trends that emerged from our research, we will be deep diving into challenges and opportunities around access to data.

 

Access to Data Is Limited: Half (51%) of end-users are not getting data at the moment they need it

There’s an influx of data being generated, but half of enterprises lack the resources to access it and use it in real-time. Our data shows that over 4 in 5 IT decision-makers (ITDMs) say one of the most painful parts of data analytics is how long it takes to deploy, yet businesses who can leverage more of their data sooner and more often for actionable insights outpace competitors who are less agile.

Almost 3 in 5 ITDMs say they have lots of data and lots of technology, but don’t believe it’s making any difference to their business. Being able to act on data in the moment is paramount to transforming business outcomes and improving the chances of business success.

Over time, data-driven advantages will establish who the key players are in every business category.

It’s imperative for long-term success to pursue the data architecture that can enable all of an enterprise’s unique data-related goals and objectives. This means being able to bring analytics capabilities to any place where a company’s data already lives, whether on-prem or in the cloud.

Organizations should be able to access the highest levels of query and ad-hoc analytics performance across the entirety of their data, and they should be able to do this while easily enforcing any required data privacy and governance policies.

 

Data That Is Available Is Not Fresh or Current: Only 26% of end-users are fully maximizing the potential actionable insights from their data

Data is being generated in the enterprise that is not being put to good, strategic use. Gaps in the system take engineers weeks or even months to wade through and bring forth something actionable. Slower decision-making is only one consequence of having to wait for data to become available for analysis, though.

Modern, aspirational analytics use cases, like customer-360 and hyper-personalization, simply don’t work with stale data.

As ML and AI become more actively involved in defining user experience, the lines are blurring between traditionally separate transactional databases and data warehouses when it comes to the need to feed data into algorithms that are making or supporting real-time decisions and automation.

Therefore, the role of “real time” data in the enterprise goes beyond internal reporting and insights and now begins to shape the customer experience, manufacturing and logistics operations, and hosts of other mission critical use cases.

Data complexity creates a barrier to entry here, though. Over two in five (45%) say the complexity of real-time data and big data present a challenge when looking to harness their data. This is largely due to the time and expense of data processing and preparation inherent in more traditional, batch-mode siloed data collection and warehousing.

Modern analytics for the enterprise are about harnessing all data, from all sources – applications, transactions, CRM and beyond. These need to be harnessed – fused together – under a common framework that can support all the demands of reporting, insight generation, and increasingly predictive analysis and decision support a business may have.

In particular, as the type and depth of insights and predictive support become the focal point, demands stemming from the operationalization of ML, AI, and algorithms within more industries and companies will require fresh hybrid data.

 

Looking for a path forward

Enterprises have long chased the promise of big data and how to leverage it to propel their businesses forward. However, what we’re currently seeing as an outcome of this chase is a lot of companies drowning in data. With the focus zeroing in on getting the most data possible, businesses have become engulfed by the sheer amount of data and are actually getting pulled further away from their data goals and aspirations as a result.

Businesses are looking for a clear path forward around how to collect, analyze, manage and use their data in the most effective ways – stay tuned for parts 2-4 of this series as we take a deeper look at this.

View our infographic.