It’s an increasingly digital, data-driven world. Data helps us make better decisions about everything from product purchases to business strategy and planning in both our personal and professional lives – the lines for which have been inextricably blurred for many of us in recent weeks as we shelter-in-place, writes Rob Woollen CEO and Co-Founder of Sigma Computing.
While data-driven decision making remains a top priority for companies, analytics and business intelligence (A&BI) adoption remains low, which may be because companies fail to give employees access to company data. Moving forward, the companies that invest in products, services and training focused on increasing data literacy and supporting data-driven strategies will outpace laggards.
So how do startups and SMBs make the leap into being a truly data-driven organization? It may seem that success stories only come out of large enterprises that have undergone massive digital transformation initiatives, the resources to manage on-premise data warehouses, and an army of IT talent and skilled SQL analysts on standby. It may seem impossible for a startup to compete when it only has at most one data engineer, maybe a SQL analyst, and definitely no budget for an extensive A&BI program.
A recent report from Deloitte polled one thousand SMBs in the US to find that 85% of all SMBs report that the digital tools they are using have helped their business in some way. Yet, more than 75% of all SMBs believe that they face at least one internal or external barrier to increasing their use of any digital tool. A&BI are a growing necessity for businesses across a spectrum of industries from real estate to retail, financial services to marketing, as data becomes a gating factor for understanding complex customer experiences and rapidly evolving competitive landscapes.
Fortunately, a lot is changing in the world of data warehouses, analytics, and business intelligence. A growing number of providers offer every level of A&BI solutions in the cloud. With these modern solutions, startups and SMBs can compete with large enterprises in a way that is affordable and scalable. Startups and SMBs seem to have three hurdles in common when it comes to building A&BI into an organization’s digital tool kit but the solutions are accessible, flexible and available today.
Hurdle 1: Disjointed Data Sources
Where does a business get data? According to one SaaS management vendor, the average SMB uses 20 paid SaaS products. Each of those SaaS applications probably generates data that is critical to at least one department in an organization.
For a technology startup, there may also be data generated from its own product(s). Your company may be collecting information about reliability and customer usage that is directly tied to decisions about the product’s future. Data tied to the company’s bottom line from payments and financial sources may come from internal sources. While SaaS apps are affordable and provide the flexibility that many startups need, they leave much to be desired in their ability to provide insights to drive business outcomes. In some cases the only way to view a report is within an app’s own system.
Most organizations, no matter their size, will want to build a model based on data from two, three, four, or 20 applications, in addition to internal data sources. Depending on the tools already in use, the employee responsible for data may use a series of work-arounds with tools they have access to that don’t require code. Any work-around solution that leverages data on hand assumes that all data sources are organized and there is no unstructured data to deal with, such as emails, videos, or social channels. As we generate more IoT and app data, this scenario becomes less and less likely.
To overcome this hurdle, you need a cloud-native provider that’s connected to a cloud data warehouse (CDW), like Snowflake. ETL (Extract, Transform, Load) vendors, like Fivetran, move data from multiple sources into a warehouse where it can be accessed, previously separate datasets can be joined, and then analyzed together.
Embracing cloud-native A&BI solutions over traditional infrastructure offers many benefits, such as greater flexibility and scalability to handle growth, lower upfront investment and improve overall performance. With a cloud-native solution, organizations will have an on-demand platform for data management without the need to engage multiple vendors.
Migrating to a cloud data analytics stack becomes an engine for innovation across your organization because it minimizes time-to-value and increases ease-of-use A&BI applications. S&P Global found that 60% of surveyed organizations say a majority of their IT will be off-premise within two years. This is a huge benefit for startups and small businesses that depend on agility as their operations grow.
Hurdle 2: Outdated Data
Real-time data coming in from SaaS applications is only useful if you can also analyze it in real time. Increasingly, some are finding that data-driven decisions based on old data can be worse than making decisions with no data. It is crucial that organizations develop a system to continuously collect, update and analyze data, in order to maximize its potential. What could be a useful business insight one day could be irrelevant or out-of-date the next.
Analytics and business teams need to move as quickly as the business is changing. If they don’t have access to data or have to wait for an analyst to write code to access the data, this lag can create a competitive disadvantage as a company can be constantly behind their competitors when analyzing and processing information.
Solution 2: Cloud Data Warehouses & Data Lakes
Data warehouses are a centralized hub where all an organization’s data resides, you can access and analyze information coming in from different sources. In addition to a data warehouse, some organizations also utilize data lakes as a place to store nonrelational data from sources like IoT or mobile devices.
On premises data warehouses are the “new legacy.” On-premises options require a large upfront financial investment. In addition they require dedicated IT and data engineering resources to maintain software and scale the system up and down depending on usage and company needs.
CDWs can scale elastically, don’t require an upfront infrastructure investment, and can manage structured and unstructured data. They allow engineering and IT resources to stay focused on core business projects. With all of the data in a dynamic warehouse, data analysts can quickly develop up-to-date dashboards, reports, and worksheets based on what is happening now, not five minutes ago, or 12 hours ago.
Hurdle 3: Hard-to-use Tools Lead to Security Risks
Business users at startups sometimes find themselves in a difficult situation. They want a simple question answered, or a simple analysis conducted, but the company’s one data analyst doesn’t have the bandwidth to deal with it quickly. It is unlikely that a business user would have access to the company’s A&BI tool, beyond a dashboard, either due to the costs associated with licenses or the data team’s desire to maintain tight control. And even if they did have access, the business user hasn’t gone through the tool’s training program and are unlikely to feel comfortable using the company’s analytics tool, especially if it requires coding or advanced SQL knowledge.
If the business user decides to take matters into their own hands, they are likely to turn to a spreadsheet, which opens them up to a variety of potential security issues. According to a PwC analysis, current employees are the top source of security incidents. Most of these employees aren’t behaving maliciously, rather they’re inadvertently exposing proprietary and personal information by downloading a spreadsheet on to their machine, or worse, sending it to the wrong person. Employees shouldn’t be driven to risky behavior because they seek information beyond what is provided to them in a static dashboard.
Security risks aside, the limitation on rows and columns in spreadsheets makes analyzing large data sets impossible and the business user runs the risk of a data analysis based on incomplete or outdated information.
Solution 3: Cloud Analytics Tools
A company’s analytics solution directly impacts how individuals within an organization will interact with data. Sometimes this involves multiple tools with varying levels of complexity. There are, however, an increasing number of tools that can be set up to fit the needs of individuals with varying technical backgrounds.
Cloud native A&BI tools help to solve this problem and those in Hurdle 1 and 2. They draw data from a CDW, so all relevant information is available to those who need it. Modern analytics tools are built with the needs of all kinds of users in mind. A spreadsheet-like interface, along with drag-and-drop components, allow anyone to conduct advanced queries without writing a single line of code. Security risks are reduced because a cloud native A&BI tool connects directly to the CDW. There is no staging area that stores data and moves it from one place to another.
Finally, because all of this analysis can be done in a browser, there is no need to save data locally. Cloud A&BI tools eliminate the need to download spreadsheet files to local PCs or email documents. Every worksheet can be accessed in a single product and securely shared with those who have permission from the data team to view it.
Data Helps Level the Playing Field
Access to cloud native solutions has a direct impact on how organizations will use data across their organizations and change the perception of data from a luxury to a necessity for business growth, competitive agility and flexibility in a perilous business period, like we find ourselves in today amidst the global COVID-19 pandemic. Adoption of cloud technology will continue to drive growth, overcoming these data management and analytics hurdles will determine whether 2020 will be a year of success or struggle for many organizations.