ETLs, ELTs, data mesh, data fabric, logical data warehouse... These concepts have been making the rounds for the last few years, but they don't seem to resonate much with the community outside the enterprise segment. As a result, data integration has remained mostly an enterprise-only affair, with companies failing to tap into the startup and SMB markets.
Maybe tech companies did not make a strong enough push to expand into other segments, but startups and SMBs looked hesitant to explore the possibilities on their own as well. What has kept them from showing more interest in bringing their data together? We investigate the common myths surrounding data integration and reveal why they do not hold water.
This is the lie most startups and SMBs tell themselves to justify their inaction. These organizations do not have a fraction of the data enterprises have to deal with every day. However, the smaller volume of data does not change the fact that whatever data you have will be the basis for your decision-making. Your employees still need access to accurate, complete, relevant, and timely data regardless of the size of your operations.
Additionally, implementing a data integration plan has very little to do with where you are in your startup journey. Hence, “it’s too early for us to think about data integration” is not a good excuse. The more you postpone the data integration issue, the bigger the problem will be in the future. When you finally decide that you finally have enough data to justify a data integration strategy, you will realize that you are sitting on top of data silos.
The life of a founder is full of moments where he needs to make important decisions, reevaluate previous ones, and look for trends in data. Data scattered far and wide over spreadsheets and SaaS tools that don't talk to each other poses another challenge for a founder. Not having a single view of truth undermines decision-making, cripples product development, and costs time and money—two resources a startup can ill afford to waste. Even simple periodic reports will take much longer to produce than they should when data is fragmented. That's why data integration should be on the agenda right from the start, and the data infrastructure should grow with the startup.
The decision on data integration usually comes down to building it in-house or buying it off-the-shelf. Both options are prohibitively expensive for startups and SMBs, which causes inertia to set in. As a result, decision-makers choose to do nothing.
Building a data integration infrastructure in-house requires a talented IT team and a lot of time to design for the integration needs of different users. The in-house solution takes an ongoing effort to establish governance procedures, rebuild broken data pipelines, and uphold security, which might be more than most SMBs signed up for.
The alternative is to buy off-the-shelf, but it also has drawbacks. Most solution packages in the market cater to enterprise needs. They cost an arm and a leg while offering features a small organization would hardly ever need. Startups and SMBs need and deserve a purpose-built solution.
Luckily, the move toward data virtualization heralds a future where employees will be able to self-serve. Data virtualization allows startups and SMBs to lean on their frontline workers to bring together their data. By getting rid of labor-intensive transformation and minimizing maintenance, data virtualization eliminates the need for a large, expensive IT team. Therefore, startups and SMBs would be well-advised to follow the developments in the self-service data integration area. As innovations like zero-ETL or other no-code data integration approaches trickle down from the enterprise segment, new opportunities will come up for business owners who would like to be self-reliant on data integration.
For now, maybe. But a data warehouse cannot satisfy the data integration needs of an organization for long in this day and age. You may be happy with your data warehouse if your data is completely structured and arranged in rows and columns. But don’t hold your breath that it will remain that way in the future. 80 percent of data today is unstructured and growing at a rate of 30 to 60 percent year over year. You can’t ignore the different formats data is stored in if you want to have a single view of truth.
A change in the game plan is necessary if we are to build a data infrastructure incorporating unstructured data. We are talking about a more dynamic environment where data flows in from a plethora of sources in various formats. Therefore, metadata, which describes the name, size, and type of data, becomes all too important. You either accept the challenge and look for ways to integrate all data you have, regardless of its location, or you neglect the need and settle for a small portion of the truth. The latter scenario is a recipe for disaster in a business environment where everyone strives to become more data-driven.
It’s only natural that people have misconceptions about a technical and intimidating field like data integration. The tech industry has so far done little to correct those misconceptions. Data integration companies were comfortable serving enterprise customers, which employed technical teams that needed no education on why data integration matters. However, if we are to bring startups and SMBs into the fold, we should start by opening their eyes to their own needs first. Everything will fall into place once regular people realize the power of data and what they can do with it.