The race to get data analytics right
This is a guest post by Sunil Chavan, vice-president for FlashBlade at Pure Storage Asia-Pacific & Japan
In evolutionary theory, the red queen hypothesis suggests that species must constantly adapt, evolve, and reproduce, in order to keep up with their competitors.
The same concept could be applied in today’s hyper-competitive business landscape. Driven by massive tectonic shifts in business models thanks to cloud computing and smart devices, entire sectors are having to embrace technology and leverage data to transform and stay ahead of their competition. Covid-19 has not only intensified this clamour, but also offered many businesses and organisations the opportunity to understand their customers, partners and stakeholders better, thanks to the massive volume of information they come across every day. By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency, according to Gartner.
Amidst crowded competition, the ability to tap this data through analytics to guide strategy and decision-making will be the key to winning this innovation arms race. We’re already seeing industry frontrunners deliver cutting-edge experiences through the use of data in increasingly creative ways, whether it’s the use of social media data to predict the next snack trends or the algorithms deployed to deliver hyper-personalised Netflix recommendations.
On the public sector front, analytics is also being used to help governments serve citizens better from leveraging CCTV information and public reports to manage flooding in Jakarta, to the tracking of shopping mall foot traffic in real-time to help Singaporeans make smarter social distancing decisions.
Countless examples like these demonstrate how analytics can deliver measurable impact regardless of the sector, and we’ve barely scratched the surface of its true potential. However, despite the clear benefits and ability to catalyse digital transformation, the path to designing a data infrastructure ripe for analytics isn’t so straightforward.
Cultural and structural shifts
The challenges that many organisations face with analytics are two-fold. Oftentimes, the problem is not about having enough data, but deciding where to begin. CIOs and IT leaders are inundated with data – a challenge that will only be exacerbated by the proliferation of connected devices. In fact, 60% of IT and data professionals regard the sheer volume of data sources as the most common data quality issue.
Secondly, most organisations’ storage environments are not set up for analytics. While the volume of data has exponentially increased alongside the capacity for developing richer insights, most of these data exist in silos, in the form of data lakes, warehouses and AI clusters.
An example of this increasingly complicated data movement is in environments like that of super apps Grab and Lazada, encompassing digital payments, e-commerce, live-streaming and much more – all requiring the integration of numerous data streams. For organisations seeking to leverage the acceleration of digital connectivity in ASEAN, a concerted effort needs to be made to design a modern data infrastructure that enables seamless data management from different sources.
Aside from structural challenges, human factors can also impede this transformation. Business and IT leaders face a range of issues such as the lack of internal alignment across business functions, or poor understanding of the processes and tools needed to store and access data. All these add layers of complexity when trying to get analytics projects off the ground.
This uncertainty is also observed among your average employees who may be unsure of their abilities to handle data as part of their regular work. With only one in five employees across the Asia-Pacific region confident in their data literacy skills, end users may continue to have trouble finding the value in analytics if they are unable to tap on data.
For teams to move past these hurdles, both cultural and structural shifts are required. The value of analytics needs to move from being descriptive to predictive and tied to business successes before it can be fully appreciated and applied across the business.
Creating a modern data platform
Building a successful analytics approach first requires an understanding of the key success factors and end-user requirements – oftentimes, it’s speed and agility that matter the most. Analytics cannot be expected to deliver timely insights and accelerate decision-making if the access and processing of data are plagued by latency issues.
In recent years, more organisations are seeing the value in moving towards flash storage as a way of simplifying and improving the performance of their databases, ensuring that teams have the data they need at their fingertips. In the last five years, a new class of flash-based storage has demonstrated the importance of a unified approach to performance, consolidation and simplicity that, when coupled with reliability and virtually no downtime, has made it a key component in kickstarting digital transformation initiatives.
As all this data changes hands, business leaders cannot overlook the importance of compliance and data security, the cement that holds any infrastructure overhaul together. Data protection needs to be built-in and simple to manage without further complicating an already complex environment. Encryption and processes must also comply with both local and international regulations. Whether the data lies in the cloud with a third-party service provider or on-premise, businesses will be held accountable when a breach occurs. To navigate this, both business leaders and employees alike must understand the flow of data through their organisation and implement the necessary measures to avoid any lapses.
Finally, getting a grip on costs is a big determining factor for any company regardless of size. The volume of data to be stored and analysed is constantly growing, with analytics and data easily becoming a significant cost centre. CIOs and teams need to focus on high impact goals that can meet or even predict business needs.
That said, CIOs need to be realistic about costs, and prioritise investments on capabilities that provide utmost agility and scalability for their data, both on-premise and in the cloud. Working with a storage-as-a-service model can be a quick way to get a scalable analytics-friendly environment started without the need for costly capital expenditure investments.
With Covid-19 not retreating anytime soon, the pivot towards the digitalisation of everything is set to continue and businesses will be forced to adapt or risk losing out, if not close shop altogether. Businesses must think about laying a modern foundation to manage enormous amounts of data by thinking about the performance, consolidation, and simplicity of their storage system in a unified fashion.
Only then will they be able to extract real value from their most-prized, under tapped asset – vital to decision-making that can propel business ahead of the competition. It will be a constant race, and one that will be won with data managed and used well.