At 0ptimus, we do big data differently. While others find insight, we find action.
As a culture, we maintain a relentless focus on tangible results—such as driving segment revenue, influencing customer perception, cutting marketing costs, winning votes in an election, or increasing foot traffic during the holidays.
For the nearly 300 projects since we have completed since our founding in 2013, we have met the goals of our clients 95% of the time. Meanwhile, industry pundits suggest that a majority of big data projects fail. So how does 0ptimus succeed over and over again in an industry where failure is nearly the norm?
A common statistic thrown around in big data circles is that more data has been created in the last several years than in the entire history of mankind to date. With this obscene, unwieldy amount of data available, it is easy to get lost in the weeds.
Companies starting big data projects need to first carefully understand the business needs for doing so. Many organizations—clients and competitors included—view insights into areas such as customer behavior, organization structure, or market sentiment as sufficient to start a project. But in our opinion, gaining “insights” alone is never cause enough to shoulder the costs of a big data assignment.
Executives need to carefully consider what outcomes they want to drive most, such as cost savings, revenue growth for a key SKU, or improved market perception. Then, and only then, can big data fit alongside the product, marketing, creative, and logistics teams to augment and enhance their capabilities. Big data is not going to find a magic answer to your business challenges, but can help you drive business outcomes with greater efficiency.
Industries are drowning in data. When beginning a big data project, it is important to think critically about exactly what data sources are necessary to answer the business need. If data is not useful to driving the business outcome, it should not be collected and analyzed.
To think through the data need, executives need to work with their internal data teams and external partners to first carefully articulate the business outcome they seek (see #1). Once the business outcome is determined, the teams need to construct the data pathway to achieving the insights, actions, and iterations needed to succeed. Data needs should be lean, efficient, and well defined.
In big data, smaller is typically better.
With industry hype around big data, data science, machine learning, and AI, it is easy to believe that big data will solve all our problems. However, analysis is only as good as the data that goes into the process. To maximize the results of big data projects, executives and data scientists alike need to understand the processes through which data is collected.
Not all data is created equal. Many times, the information being collected by an organization is insufficient to meet the data needs of the assignment. In these cases, new collection methods will have to be developed. Sometimes this means integrating information that is held elsewhere in the organization, while other times it means creating entirely new data pipelines.
There is bias in almost any data pipeline, simply because it was creating with a certain intention. Knowing this, it is important to drill down all the way to exactly where and how data is collected, working from the collection process to work through any biases.
At 0ptimus, we make sure that all data collection processes can relate back to our business unit. Typically, this is information about individual customers, voters, consumers. Therefore, our collection methods align with these needs.
Despite our efforts—and the efforts of others in the industry—to create turnkey solutions using big data, no such software capability presently exists. Big data projects require constant iterations to get to an answer. Therefore when establishing the goals of a big data team, it is essential that executives and managers set up the goals as static, but allow the team to drive to these solutions using an iterative, experimental process.
At 0ptimus, we are experimental experts that can create solutions for you.
Data science’s role in business is expanding, changing, and evolving. Organizations need to forge relationships with big data practitioners they trust in order to generate results. Unfortunately, like any other booming industry, big data has its fair share of fly by night operators draining money from those who don’t know better to no particularly valuable end. It is essential to do your homework, choose somebody who is both trustworthy and a good fit for your individualized needs, and give them plenty of runway so that they can help make your business great again.
0ptimus has a long and deep track record in extremely high stakes spaces that has motivated many of our clients to keep coming back to us for their data needs.
If you want to learn more about how to set your team up for big data successes, learn more about 0ptimus Big Data Days. During a 1-day workshop or 3-4 week sprint, we explore how big data can drive real business outcomes, how big data teams can be built and managed, and develop real-world tests for your market.