Big data isn’t just about large data sets. Big data was never meant to be only about the size of the data. In addition to data volume, the variety and velocity of data are necessary to understand why and how information can be analyzed, captured and learned from. Big data is about combining this messy data, applying analytics to it to glean answers to pertinent questions. Without analytics, big data is just a lot of messy and often, dangerous data you have to store and keep away from inappropriate eyes.
Now, since we often associate this big data and analytics marketplace exclusively with big data sets, we end up typically associating it with big enterprises. This notion has made it hard for midsize companies to find relevant information on how they can leverage big data to enhance customer satisfaction, improve insight and positively influence the bottom line.
What are the opportunities offered by big data analytics to the midmarket?
With the help of advanced analytics, you can leverage your data to glean insights that help you be less reactive and more predictive to change, reducing risk and making way for more targeted marketing. Leveraging enterprise data successfully has led to the improved overall performance and given many a competitive edge. Big data and analytics can help most midsize organizations, with:
Streamlining supply chains, increasing efficiency, detecting where operational processes can be improved and uncovering fraud proactively.
Most mid-market companies could use the insight, visibility, and control over financial operations provided to transform their financial processes.
Assisting businesses to manage regulatory and compliance change, by taking considerable risk out of the equation.
Increase IT ability to react more quickly to change, flexibility and agility while improving IT economics.
The big data analytics adoption journey
Although many businesses see the potential in their data stores, they lack the skills and the infrastructure necessary to seize the opportunity and to boot, are often strapped for financial resources. And others simply don’t know where to start. The many hurdles and questions that arise can be harder for the resource-challenged, smaller organizations to answer. The below action items are a good place to begin.
Figure out what business outcomes matter the most to your company.
Start the conversation around big data.
Taking into consideration both business leaders and the IT infrastructure team, identify which stakeholders and decision makers need to be involved, and make sure you include them early on in the discussions.
Jot down a list of discussion points and questions.
Set up meetings with stakeholders.
Through the process don’t lose sight of the main goal of all big data analytics initiatives, revealing insight that translates to business value.
What to keep in mind while defining the roadmap?
Getting started with big data implementation will seem a lot like assembling a jigsaw puzzle. The hardest part will be finding out what pieces will help you build the edges of the puzzle. Putting the last few pieces in place will be a lot easier. Deciding which data to start with, which projects to tackle first and where to acquire the skills, the sponsorship and the funding are going to be hard questions to answer.
A roadmap can help you improve decision making by paving the way for a better understanding of how you operate, what your customers expect, and what direction the marketplace is headed. Big data analytics is most probably a huge shift from the way business is typically done at your end. Therefore many organizations, SMBs in particular, will need to fill analytic skills gaps by either partnering with a trusted partner or developing skills in-house to better prepare for those hurdles. So where do you begin?
Start with existing data. You have to be realistic if you’re looking for relatively fast near-term value, meaning starting at the most cost-effective and logical place, existing enterprise data. Start with your internal data, leverage existing software, systems, and skills. Quantifiable business results and a positive experience will encourage future initiatives, paving the way for more complex projects and an expansion in the varieties and volumes of data.
Big data projects don’t succeed in a vacuum. Collaboration is key. Forge shared goals on big data initiatives, keeping lines of communication open, especially between IT and executive sponsors. Constructive, ongoing dialogues will increase the chances of success.
To move efforts from “data for data’s sake” projects or “data science projects,” to strategic initiatives based on real business objectives, invest in good analytics tools and experienced engineers and data scientists.
There is a widening gap between analytical skills and opportunities in most organizations as big data analytics increases in importance. Starting with employees in touch with the company’s unique objectives and challenges, development of in-house analytic skill sets must be a priority. You can enhance these skills through strategic outsourcing with experienced, trusted partners.
This point warrants a repeat, work toward measurable goals. A quantifiable business case aligned to business objectives is necessary for a solid big data analytics strategy. So the importance of sponsorship from executives on the top who can collaborate with IT and other key executives will greatly influence your chances of success.
Each big data analytics project differs. Work towards the right time frame for your project, by taking into consideration infrastructural capabilities, skills and data volumes to help set realistic expectations.
In the absence of analytics, big data becomes noise. Build a culture that infuses analytics into every step of business operations, ensure that governance and security of data is paramount, and make the necessary investment in a platform that is tuned to big data and analytics and your businesses will be able to successfully leverage your data for the insights needed to transform the way you do business. To reach there, allow for a fact-based culture, one that infuses every point in the decision making process with big data analytics. Failing this will make creating value from big data and analytics investments hard, if not impossible. A culture of analytics means that decisions are made with the help of data, not intuition. Good Luck!